The accelerating deployment of generative AI tools, from design assistants to complex financial agents, is driving massive infrastructure investment and exposing critical security gaps. Concerns are mounting over the computational demands of these systems and the potential for Remote Code Execution vulnerabilities within AI protocols. This signals an urgent need for robust security standards across the evolving AI ecosystem.
Anthropic launches Claude Design, a tool for creating interfaces, prototypes, slides, and marketing assets using natural language prompts.
Anthropic has just unveiled Claude Design, a groundbreaking tool that turns natural language prompts into polished interfaces, prototypes, and marketing materials. This launch is significant because it leverages the latest Claude Opus 4.7 model, which is optimized for visual tasks and professional design work. Users can now upload brand assets or pull elements from company websites to ensure consistency across outputs. As AI continues to bridge the gap between idea and execution, tools like Claude Design are redefining how teams create and collaborate. How will your organization adapt its design and development processes to leverage these new capabilities?
Adobe introduces Firefly AI Assistant, enabling multi-step workflow automation across creative apps like Photoshop, Premiere, and Illustrator.
Adobe is transforming Firefly into a true creative AI studio with its new Firefly AI Assistant. This update allows users to describe desired outcomes and have the system plan and execute multi-step workflows across Adobe’s creative suite. With upgrades to video editing, image precision tools, and support for over 30 AI models, Adobe is pushing the boundaries of what’s possible in generative AI. For designers and content creators, this shift from tools to agents marks a pivotal moment in how we approach creative workflows. What does this mean for the future of human-AI collaboration in your field?
Google introduced A2UI 0.9, a generative UI framework allowing AI agents to build dynamic interfaces from existing code.
Google’s new **A2UI 0.9** framework is redefining how AI agents interact with existing codebases, enabling them to dynamically generate and modify user interfaces. By leveraging the framework’s shared web core and Agent SDK, developers can now deploy AI agents that “speak UI,” creating interactive interfaces without arbitrary code execution risks. This innovation could dramatically accelerate the adoption of AI agents in software development, bridging the gap between AI assistance and real-world implementation. How will this shift impact your team’s approach to software development and AI integration?
Meta plans to lay off 8,000 employees, approximately 10% of its workforce, to fund a $135 billion AI infrastructure expansion.
Meta is set to **lay off 8,000 employees**, roughly 10% of its workforce, as CEO Mark Zuckerberg reallocates capital toward a **$135 billion AI infrastructure push**. This dramatic shift highlights the growing prioritization of AI over traditional headcount, signaling a new era where compute and automation trump human labor in certain areas. The move also reflects the company’s bet on AI-assisted labor and autonomous agents. How will this reallocation of resources influence the broader tech industry’s approach to hiring and AI adoption?
Google is in talks with Marvell to develop two new AI chips: a memory processing unit for TPUs and a new inference-focused TPU.
Google is advancing its AI chip ambitions with talks to collaborate with Marvell on two new AI-specific processors: a memory processing unit for TPUs and a dedicated inference-focused TPU. This move aims to enhance Google’s custom AI hardware stack, addressing both training and deployment bottlenecks. For companies investing in AI infrastructure, this signals a trend toward specialized, optimized chips. How will these advancements impact the cost and accessibility of AI deployment for businesses?
Stripe and Airwallex transitioned from a potential acquisition deal to direct competition in global financial infrastructure.
The fintech landscape just got a lot more interesting as Stripe and Airwallex shift from acquisition talks to full-scale competition. Airwallex’s decision to reject a $1.2B offer early on has paid off, with the company now scaling to over $1.3B in revenue by building deep global infrastructure across 50 markets. This clash highlights a critical divide: API-driven platforms versus full-stack financial systems built on proprietary rails and data. For businesses, this means more choices—and higher stakes—in financial workflow automation. How will your strategy adapt as these platforms race to own the end-to-end financial stack?
Slash Financial raised $100M at a $1.4B valuation, competing directly with Ramp and Brex.
Slash Financial’s $100M raise at a $1.4B valuation underscores the enduring appeal—and urgency—of modern finance stacks. With $300M in annualized revenue and 5,000 business customers, the company is carving out a space in the crowded spend and banking platform market. What’s most striking is its pivot from a niche focus (sneaker resellers) to a generalist platform, mirroring the strategic flexibility that defines today’s fintech leaders. As Ramp and Brex push for dominance, Slash’s growth signals a maturing market where specialization and scale both matter. Where do you see the biggest gaps in today’s corporate finance tools?
Ramp expects to reach $1.4B in annual recurring revenue ahead of a potential IPO.
Ramp’s projected $1.4B in annual recurring revenue is more than just a number—it’s a statement about the future of corporate finance. Doubling revenue in six months while preparing for an IPO highlights the explosive demand for AI-driven spend management platforms. The acceleration isn’t just about growth; it’s about redefining how companies control costs and leverage automation. As Ramp solidifies its lead, the question for incumbents and challengers alike is: Can traditional finance tools keep up with the pace of innovation? What’s your take on the next phase of corporate finance evolution?
Vertical AI’s most valuable opportunities are emerging in fragmented, operationally complex industries.
Vertical AI isn’t just about narrow tools anymore—it’s about owning entire workflows in messy, fragmented industries. The companies that win here are shifting from point solutions to full ‘systems’ that integrate data, processes, and labor budgets. This isn’t just a product shift; it’s a fundamental rethinking of defensibility. As horizontal AI labs struggle to scale, vertical players are building moats that compound over time. For founders and investors, the message is clear: depth beats breadth. Where do you see the next big vertical AI opportunity?
Tokenization and AI are reshaping financial infrastructure by making money programmable.
We’re witnessing the birth of a new financial paradigm: money as programmable tokens. From stablecoins to CBDCs, these assets enable real-time settlement and autonomous financial systems where AI agents can manage, move, and optimize capital with minimal human input. When combined with AI, this isn’t just incremental change—it’s the foundation for a self-optimizing financial stack. The implications for banking, payments, and corporate treasury are profound. How soon will your industry be disrupted by programmable money?
Ramp achieved 99% AI adoption internally but built an AI suite called Glass to bridge usage gaps.
Ramp’s internal AI journey is a masterclass in turning adoption into impact. After hitting 99% AI usage company-wide, they realized most employees were stuck at basic levels without complex setups. Their solution? Glass—a unified AI suite that connects to all enterprise tools via single sign-on and provides a marketplace of 350+ reusable ‘skills.’ This isn’t just about tooling; it’s about rewiring how work happens. For enterprises drowning in AI pilots, Ramp’s playbook offers a blueprint: infrastructure is the moat. How are you removing friction from AI adoption in your organization?
American Express is acquiring Hyper, an AI-native expense management startup, to build agent-driven finance tools.
American Express’s acquisition of Hyper signals a tectonic shift in expense management. By embedding AI agents directly into spend workflows—from travel to reconciliation—Amex is betting big on automation to outpace modern fintech players. This isn’t just about incremental efficiency; it’s about redefining the expense stack as a fully integrated, AI-powered platform. With prior moves like the Center acquisition, Amex is positioning itself as a leader in the next generation of corporate finance tools. How will incumbents and challengers alike need to adapt to survive this AI-driven transformation?
Airwallex launched a physical point-of-sale device to expand into in-store commerce.
Airwallex isn’t just competing in digital payments anymore—it’s taking the fight offline with a new physical POS device. By unifying offline and digital transactions under its broader financial platform, Airwallex is positioning itself against incumbents like Square and Stripe with a unique edge: owning both banking infrastructure and software in key markets. This move highlights a broader trend: fintechs are no longer content to be digital-only players. As commerce becomes omnichannel, how will your business strategy adapt to this convergence?
BNY is repositioning itself from a traditional financial services provider to an embedded AI platform within client workflows.
BNY’s pivot from traditional banking to an embedded AI platform is a wake-up call for the entire industry. By integrating AI into data, processes, and decision-making at scale, the bank is redefining competitive advantage beyond balance sheet strength. The results speak for themselves: faster onboarding, improved compliance, and a surge in productivity. This isn’t just about cost-cutting; it’s about building the operating system for the next era of finance. For both incumbents and challengers, the message is clear: platform capabilities will separate the leaders from the laggards. How are you embedding AI into the core of your business?
Citadel Securities is exploring entry into prediction markets as a liquidity provider for non-sports use cases.
Citadel Securities’ potential move into prediction markets could be a game-changer for the sector. With $51B in 2025 volume and projections to hit $1 trillion annually by 2030, this isn’t a niche bet—it’s a bet on the future of risk management. By focusing on non-sports use cases like geopolitical and macro risk hedging, Citadel is positioning itself at the intersection of finance and prediction, where liquidity is king. As regulatory clarity improves, the floodgates could open. How will this shift reshape how institutions manage uncertainty?
Over a dozen U.S. states are considering temporary bans on new data center development due to energy and infrastructure concerns.
The data center dilemma is reaching a boiling point. With over a dozen U.S. states considering temporary bans on new development, the tech industry faces a reckoning: growth at what cost? Concerns over energy use, environmental impact, and strain on local infrastructure are colliding with the insatiable demand for AI compute. Maine is leading the charge, but it won’t be the last. For companies scaling AI infrastructure, this is a critical inflection point. How will your organization adapt to a world where compute resources are no longer a given?
Fintechs are reshaping customer expectations, forcing traditional banks to modernize or risk irrelevance.
Fintechs didn’t just enter banking—they redefined it. Faster onboarding, transparent pricing, and real-time payments are no longer differentiators; they’re the baseline. Now, the pressure is on traditional banks to either modernize or become irrelevant. The challenge is existential: can incumbents with decades of legacy systems and processes pivot fast enough to meet the expectations set by agile fintechs? For SMEs and consumers alike, the message is clear: the best financial partner isn’t necessarily the one with the deepest roots. How are legacy institutions closing the modernization gap?
Airflow 3 introduces first-class support for agentic workflows with features like persistent task state, human-in-the-loop approvals, and dynamic task mapping.
Airflow 3 is redefining what's possible in workflow orchestration with first-class support for agentic systems. Features like persistent task state, human-in-the-loop approvals, and dynamic task mapping are specifically designed for the complex, multi-step applications emerging in the AI agent era. This isn't just about making workflows more reliable—it's about enabling entirely new categories of applications. As AI agents become more sophisticated, how are you adapting your orchestration tools to handle their unique requirements?
Vercel confirmed unauthorized access to internal systems impacting a subset of customers.
Vercel has confirmed a significant security breach affecting a subset of its customers, with attackers claiming access to internal databases, employee accounts, and tokens. This incident underscores the persistent threat of unauthorized access in cloud platforms and the need for robust credential management and continuous monitoring. For teams leveraging platforms like Vercel, this is a stark reminder to review integration permissions, rotate credentials, and audit API access. How can organizations better balance agility with security in an era of increasing cloud adoption?
Atlassian will collect metadata and in-app data from Jira and Confluence for AI training starting August 17.
Atlassian is mandating metadata collection from Jira and Confluence for AI training starting August 17, with limited opt-out options for most users. This policy shift raises important questions about data ownership, transparency, and compliance in the age of AI-driven tools. While Enterprise customers may have exemptions, the move highlights the growing trend of leveraging user data for AI model improvement. How can enterprises ensure ethical data usage while still benefiting from AI advancements?
Google Cloud is shifting from AI experimentation to always-on, outcome-driven AI systems.
Google Cloud is doubling down on AI as the core execution layer, moving from experimentation to always-on, outcome-driven systems. This full-stack control across models, compute, and data represents a fundamental shift in enterprise architecture. For businesses, this means rethinking how AI integrates into core operations rather than existing as siloed tools. How will this transition impact traditional software development lifecycles and infrastructure planning?
Researchers identified critical RCE vulnerabilities in the Model Context Protocol (MCP) due to unsafe STDIO defaults.
A critical design flaw in the Model Context Protocol (MCP) has exposed thousands of servers and 200+ open-source projects to Remote Code Execution vulnerabilities. The issue stems from unsafe STDIO command defaults, serving as a cautionary tale about the unintended consequences of rapid AI tooling development. As AI agents become more integrated into systems, how can we balance innovation with rigorous security validation?
Stellantis and Microsoft announced a five-year AI partnership covering over 100 AI initiatives.
Stellantis and Microsoft have forged a five-year strategic partnership encompassing over 100 AI initiatives, cybersecurity improvements, and a migration to Microsoft Azure. This collaboration signals a major shift in automotive IT infrastructure and AI-driven engineering processes. As traditional industries increasingly adopt cloud-first strategies, how will this reshape talent requirements and competitive dynamics in the automotive sector?
AI startups are reporting inflated Annual Recurring Revenue (ARR) due to long-term enterprise deals that can be reported now but opted out of after 12 months.
A significant concern is emerging in the AI startup ecosystem: inflated Annual Recurring Revenue (ARR) figures driven by long-term enterprise deals that can be reported now but opted out of after 12 months. This practice not only distorts financial reporting but also masks potential margin pressures, particularly when full-time forward-deployed engineers are bundled into these deals. As customers begin to negotiate or trigger opt-out clauses, we may see a wave of collapses among enterprise AI companies. How can founders and investors ensure transparency and sustainability in revenue reporting amidst this growing trend?
The hiring challenge of evaluating domain expertise you lack can be addressed by focusing on whether candidates generate ideas that reshape your thinking.
Hiring someone better than you is hard not just because of ego, but because you lack the domain expertise to evaluate them properly. The solution? Look for candidates who generate ideas that reshape how you think about your company and industry. If meeting with them feels more like learning than assessing, you're on the right track. The sharpest gut check is simple: if you'd rather do the work yourself, you've likely hired the wrong person. How do you approach hiring for roles where you lack deep expertise?
The 2% inflation target was established by a New Zealand finance minister in the 1980s during a TV interview and was adopted by central banks worldwide.
Did you know the 2% inflation target, which shapes the cost of capital for every founder raising a round, was pulled out of thin air? In the 1980s, a New Zealand finance minister suggested it during a TV interview to influence public expectations, and every central bank simply copied it. This arbitrary number has profound implications for monetary policy, venture funding, and economic growth. Perhaps it's time to rethink our assumptions about economic progress. How might a different inflation target reshape the startup ecosystem and capital markets?
Linear's CEO argues that current AI design tools incorrectly assume design is merely the act of producing an interface.
Linear's CEO is pushing back against the current wave of AI design tools, arguing that they fundamentally misunderstand design. Most tools assume design is simply the act of producing an interface, ignoring the strategic and conceptual work that precedes it. This critique highlights a critical gap in today's AI-driven design landscape: tools that focus solely on output miss the deeper value of thoughtful design thinking. How can we build AI tools that enhance the creative process rather than just accelerate production?
Reddit's iOS app beta introduces Apple's Liquid Glass design system, aligning with iOS 26's visual language.
Reddit's upcoming iOS app update will adopt Apple's Liquid Glass design system, signaling a broader industry shift toward Apple's evolving visual language. This polished implementation, currently in beta via TestFlight, demonstrates how third-party apps are aligning with iOS standards to enhance user experience. As designers, we’re seeing a trend where consistency across platforms isn’t just preferred—it’s expected. How do you balance platform-specific design guidelines with maintaining brand identity in a rapidly evolving ecosystem?
iOS 26.4 introduces new Ambient Music widgets for the Home Screen, allowing users to start mood-based playlists directly from the Home Screen.
Apple’s iOS 26.4 update brings Ambient Music widgets to the Home Screen, enabling users to instantly launch mood-based playlists like Chill, Productivity, or Sleep. This feature, limited to Apple Music, highlights how personalization and convenience are becoming central to mobile UX. For product teams, it underscores the importance of integrating contextual, user-centric features that anticipate needs rather than requiring manual input. How can your product leverage ambient computing to create more seamless, intuitive user experiences?
AI assistants are leveraging folder instructions to automate tasks and create persistent, context-rich environments within file systems.
AI assistants are transforming folders from passive storage into dynamic, self-managing workspaces by using 'Folder Instructions' to define purpose and behavior. This shift from chat-based commands to structured, always-on workflows could redefine how we interact with digital environments. However, it also introduces challenges around computational demands and rule clarity. How might this change the way we organize and prioritize work in a world where our tools are increasingly autonomous?
Washington Square Park Conservancy unveiled a new brand identity by Pentagram, combining playful illustrations with a functional website to reflect the park's community spirit.
Pentagram has given Washington Square Park a vibrant new brand identity that captures the park’s lively, community-driven spirit through playful illustrations and a refreshed website. The rebrand isn’t just about aesthetics; it’s about strengthening the connection between people and the park’s cultural legacy. For designers, this project is a reminder that great work often starts with a deep understanding of the audience and context. How can we, as designers, better balance creativity with meaningful community impact in our projects?
The Queen Elizabeth Trust has launched with £40 million in government backing.
A landmark initiative has been launched today with the Queen Elizabeth Trust securing £40 million in government funding. This investment signals a significant commitment to scaling charitable efforts across the UK, particularly in areas that may have been underfunded historically. The Trust’s mission to support community-focused organizations could redefine how large-scale philanthropy operates, emphasizing measurable impact and systemic change. For leaders in the nonprofit sector, this underscores the growing importance of strategic partnerships between government and charitable foundations. How can more organizations position themselves to leverage such transformative funding opportunities?
A historic education charity worth £5.3 million has transferred its assets to a community foundation.
In a bold move for the nonprofit sector, a historic education charity valued at £5.3 million has completed its transfer to a community foundation. This transition reflects a growing trend where legacy institutions are realigning their missions to better serve local needs, ensuring long-term sustainability and relevance. For trustees and executives, this serves as a case study in succession planning and the strategic value of community-led governance. The shift also raises questions about how other legacy charities might adapt to changing societal demands. What steps should boards take today to future-proof their organizations for tomorrow’s challenges?
Charities have been urged to call out extremism following synagogue arson attacks.
The recent arson attacks on synagogues have prompted urgent calls for charities to take a stand against extremism. This moment demands collective action from the nonprofit sector to foster inclusivity and protect vulnerable communities. Charities, as trusted voices in society, have a unique role in modeling tolerance and countering divisive narratives. For leaders, this is a reminder that mission-driven organizations must also be vigilant in addressing threats to social cohesion. How can your organization integrate anti-extremism efforts into its broader social impact strategy?
Centrepoint has distanced itself from Sharon Osbourne following a controversial social media post related to a Tommy Robinson rally.
Centrepoint, a leading youth homelessness charity, has publicly distanced itself from Sharon Osbourne after she shared a post about a Tommy Robinson rally. This situation highlights the delicate balance nonprofits must strike when associated figures make divisive statements. In an era where social media amplifies every connection, organizations must have clear policies on public endorsements and affiliations. For leaders, this underscores the need for proactive reputation management and crisis protocols. How can nonprofits safeguard their brand while maintaining relevance in an increasingly polarized public discourse?
An analyst predicts a CPU supply crisis by 2027-2028 due to the exponential growth in AI agent API calls requiring increased compute demand.
A new forecast suggests the AI agent boom could trigger a CPU supply crisis by 2027-2028. Unlike traditional chatbot interactions, AI agents chain multiple API calls per task, multiplying compute demand exponentially. Current datacenter capacity planning didn’t account for this ‘agentic multiplier’ effect, and even aggressive expansion by hyperscalers may fall short. As AI agents move from pilot projects to enterprise-scale deployments, the infrastructure bottleneck shifts from models to raw compute availability. How should your organization be stress-testing its infrastructure roadmap for this coming wave?
A new benchmark, QuantCode-Bench, evaluates LLMs on real-world algorithmic trading strategies beyond basic code generation.
Most code benchmarks stop at whether an LLM writes correct Python. QuantCode-Bench goes further: it tests whether the generated trading strategy actually executes and performs. In industries where AI touches real-world actions—like finance—syntactic correctness isn’t enough. This benchmark sets a new standard: agents must not only generate code, but prove it works in production. For teams building execution agents, this is a wake-up call. Are your evaluation pipelines measuring what truly matters?
OpenTelemetry's declarative configuration spec has reached stability for core components including JSON schema, YAML format, and in-memory model.
OpenTelemetry has reached a significant milestone with the stabilization of its declarative configuration spec. Teams can now define traces, metrics, and logs in a single, version-controlled YAML file instead of juggling environment variables and SDK init code. This vendor-neutral approach works across C++, Go, Java, JavaScript, and PHP, with more languages coming soon. In an era where observability complexity often scales faster than our ability to maintain it, this standardization could be a game-changer. What's the biggest observability challenge you're facing in your current stack?
Meta is rolling out post-quantum cryptography internally with a staged approach including crypto inventorying and hybrid deployment.
Meta is already moving ahead of the curve on post-quantum cryptography, internally rolling out encryption using NIST-approved standards like ML-KEM (Kyber) and ML-DSA (Dilithium). Their staged approach—starting with crypto inventorying and hybrid deployment—recognizes that the 'store now, decrypt later' risk is real and growing. As quantum computing advances, this kind of proactive stance will become increasingly critical. How prepared is your organization for the post-quantum transition?
Arize Data Fabric writes AI agent traces into Iceberg tables in Google BigQuery for performance and cost analysis with standard SQL.
AI agent performance tracking just got more accessible with Arize Data Fabric's new approach—writing agent traces directly into Iceberg tables in BigQuery. This means teams can now analyze AI performance, cost, latency, and business impact using standard SQL, without complex integrations. As AI agents become more prevalent in production systems, having this kind of observability will be crucial. What's the biggest challenge you're facing in monitoring your AI agent deployments?
Humanoid robot Flash completed the Beijing E-Town Half Marathon in 50 minutes and 26 seconds, surpassing the human world record by over six minutes.
This weekend, a humanoid robot named Flash shattered expectations by completing the Beijing E-Town Half Marathon in just 50 minutes and 26 seconds—over six minutes faster than the human world record. Built by Honor, Flash demonstrated AI-driven endurance, strategic pit stops, and even a dry-ice cooling system, all while showcasing the rapid progress in robotics. This isn't just a victory for engineering; it signals a shift toward AI systems that can operate autonomously in real-world environments. As robots like Flash push the boundaries of physical performance, how soon will we see them integrated into logistics, healthcare, or emergency response? What challenges remain in scaling such technology for broader applications?
Anthropic launched Claude Design, a Figma competitor powered by Opus 4.7, enabling users to collaborate on visual design projects via AI.
Anthropic has entered the design software space with the launch of Claude Design, a tool that leverages Opus 4.7’s vision capabilities to enable AI-assisted design collaboration. Unlike traditional design tools, Claude Design integrates with codebases to build persistent design systems and exports designs to formats like Canva, PDF, and PPTX. This move underscores the growing convergence of AI and creative workflows, challenging incumbents like Figma. For businesses, this could mean faster prototyping and more dynamic design processes. How do you see AI-driven design tools reshaping creative workflows in your industry?
Anthropic’s COO Cliff Obrecht announced Canva AI 2.0, enabling users to generate editable vector designs from natural language descriptions.
Canva has transformed into an 'AI platform with design tools' with the launch of Canva AI 2.0. Now, users can describe complex designs—like a 12-page Morocco planning deck—in plain English and receive editable vector outputs. This shift from a design tool with AI features to an AI tool with design capabilities reflects a broader industry trend. For professionals, this could democratize high-quality design, reducing reliance on specialized skills. How will this evolution impact traditional design roles and workflows in your field?
Sakana AI launched a browser-based artificial-life playground called 'Digital Ecosystems,' where CNN species compete on a 2D grid.
Sakana AI has unveiled 'Digital Ecosystems,' a browser-based artificial-life playground where CNN species compete for territory on a 2D grid. Users can draw walls, seed species, and tune over 40 parameters to observe self-stabilizing systems at the 'edge of chaos.' This experiment blends AI research with interactive play, offering insights into emergent behaviors and adaptive systems. For technologists, this could inspire new approaches to optimization and swarm intelligence. What lessons from nature’s complexity can we apply to building more resilient AI systems?
Three senior OpenAI executives announced pre-IPO departures: Bill Peebles (Sora), Kevin Weil (Science), and Srinivas Narayanan (B2B Apps).
OpenAI has seen a significant shakeup this week with the pre-IPO departures of three senior executives: Bill Peebles (Sora), Kevin Weil (Science), and Srinivas Narayanan (B2B Apps). Dubbed 'Liberation Day' by insiders, this exodus raises questions about internal strategy, leadership, and the company’s future direction. For the broader tech community, it underscores the volatility and high stakes in the AI race. What does this leadership turnover signal about OpenAI’s priorities and challenges ahead?
Sana AI Summit 2026 will take place on May 21 at The New York Public Library, featuring speakers like Tyler Cowen, Anton Osika, and BenjamÃn Labatut.
Mark your calendars: The Sana AI Summit 2026 will return on May 21 at The New York Public Library, bringing together thought leaders like economist Tyler Cowen, Lovable co-founder Anton Osika, and author BenjamÃn Labatut. This summit has a history of fostering interdisciplinary discussions on AI’s impact, making it a must-attend for anyone shaping the future of technology. How can events like this bridge gaps between research, policy, and industry to drive meaningful progress?
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