Dutch regulators have fined a cryptocurrency service provider for operating in the Netherlands without the legally required registration, underscoring intensifying enforcement across Europe’s digital asset sector.
De Nederlandsche Bank (DNB) originally imposed an administrative penalty of €2,850,000 on 2 October 2023. Authorities found the firm breached the Anti-Money Laundering and Anti-Terrorist Financing Act by offering unregistered crypto services.
Registration rules, introduced on 21 May 2020, require providers to notify supervisors due to elevated risks linked to transaction anonymity and potential misuse for money laundering or terrorist financing.
Non-compliance prevented the provider from reporting unusual transactions to the Financial Intelligence Unit-Netherlands. Regulators weighed the severity, duration, and culpability of the breach when determining the penalty amount.
Legal proceedings later altered the outcome. The Court of Rotterdam ruled on 19 December 2025 to reduce the fine to €2,277,500 and annulled the earlier decision on objection.
DNB has since filed a further appeal with the Trade and Industry Appeals Tribunal, leaving the case ongoing as oversight shifts toward MiCAR licensing requirements introduced in December 2024.
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Meta has introduced a new group of Facebook features that rely on Meta AI to expand personal expression across profiles, photos and Stories.
Users gain the option to animate their profile pictures, turning a still image into a short motion clip that reflects their mood instead of remaining static. Effects such as waves, confetti, hearts and party hats offer simple tools for creating a more playful online presence.
The update also includes Restyle, a tool that reimagines Stories and Memories through preset looks or AI-generated prompts. Users may shift an ordinary photograph into an illustrated, anime or glowy aesthetic, or adjust lighting and colour to match a chosen theme instead of limiting themselves to basic filters.
Facebook will highlight Memories that work well with the Restyle function to encourage wider use.
Feed posts receive a change of their own through animated backgrounds that appear gradually across accounts. People can pair text updates with visual backdrops such as ocean waves or falling leaves, creating messages that stand out instead of blending into the timeline.
Seasonal styles will arrive throughout the year to support festive posts and major events.
Meta aims to encourage more engaging interactions by giving users easy tools for playful creativity. The new features are designed to support expressive posts that feel more personal and more visually distinctive, helping users craft share-worthy moments across the platform.
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Russia’s State Duma has passed legislation establishing procedures for the seizure and confiscation of cryptocurrencies in criminal investigations. The law formally recognises digital assets as property under criminal law.
The bill cleared its third reading on 10 February and now awaits approval from the Federation Council and presidential signature.
Investigators may seize digital currency and access devices, with specialists required during investigative actions. Protocols must record asset type, quantity, and wallet identifiers, while access credentials and storage media are sealed.
Where technically feasible, seized funds may be transferred to designated state-controlled addresses, with transactions frozen by court order.
Despite creating a legal basis for confiscation, the law leaves critical operational questions unresolved. No method exists for valuing volatile crypto assets or for their storage, cybersecurity, or liquidation.
Practical cooperation with foreign crypto platforms, particularly under sanctions, also remains uncertain.
The government is expected to develop subordinate regulations covering state custody wallets and enforcement mechanics. Russia faces implementation challenges, including non-custodial wallet access barriers, stablecoin freezing limits, and institutional oversight risks.
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Researchers have advanced an AI system designed to detect bacterial contamination in food, dramatically improving accuracy and speed. The upgraded tool distinguishes bacteria from microscopic food debris, reducing diagnostic errors in automated screening.
Traditional testing relies on cultivating bacterial samples, taking days, and requiring specialist laboratory expertise. The deep learning model analyses bacterial microcolony images, enabling reliable detection within about three hours.
Accuracy gains stem from expanded model training. Earlier versions, trained solely on bacterial datasets, misclassified food debris as bacteria in more than 24% of cases.
Adding debris imagery to training eliminated misclassifications and improved detection reliability across food samples. The system was tested on pathogens including E. coli, Listeria, and Bacillus subtilis, alongside debris from chicken, spinach, and cheese.
Researchers say faster, more precise early detection could reduce foodborne outbreaks, protect public health, and limit costly product recalls as the technology moves toward commercial deployment.
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Qiddiya City, a purpose-built entertainment, sports and cultural destination covering nearly three times the area of Paris, involves more than 700 companies and 22,000 workers, with thousands of assets and complex data flowing across teams.
Microsoft 365 Copilot has been integrated into Qiddiya Investment Company’s project dashboards to help employees query data in natural language, saving time on reporting, email summarisation and document creation.
The technology also helps harmonise information across 20 different systems used by design and execution teams, simplifying tasks such as matching asset names and identifying discrepancies.
Copilot can extract insights that static dashboards miss, for example, flagging overdue invoices without engineer comments, enabling more informed decision-making.
QIC’s workforce has adopted Copilot for productivity beyond construction management, with the tool generating hundreds of thousands of emails and meeting summaries.
AlAli emphasises that careful planning and implementation are key to realising the benefits of AI at scale, underscoring how AI can provide an ‘unfair advantage’ when properly embedded into workflows for data-intensive projects.
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The Olympic ice dance format combines a themed rhythm dance with a free dance. For the 2026 season, skaters must draw on 1990s music and styles. While most competitors chose recognisable tracks, the Czech siblings used a hybrid soundtrack blending AC/DC with an AI-generated music piece.
Katerina Mrazkova and Daniel Mrazek, ice dancers from Czechia, made their Olympic debut using a rhythm dance soundtrack that included AI-generated music, a choice permitted under current competition rules but one that quickly drew attention.
The International Skating Union lists the rhythm dance music as ‘One Two by AI (of 90s style Bon Jovi)’ alongside ‘Thunderstruck’ by AC/DC. Olympic organisers confirmed the use of AI-generated material, with commentators noting the choice during the broadcast.
Criticism of the music selection extends beyond novelty. Earlier versions of the programme reportedly included AI-generated music with lyrics that closely resembled lines from well-known 1990s songs, raising concerns about originality.
The episode reflects wider tensions across creative industries, where generative tools increasingly produce outputs that closely mirror existing works. For the athletes, attention remains on performance, but questions around authorship and creative value continue to surface.
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Generative AI tools saw significant uptake among young Europeans in 2025, with usage rates far outpacing the broader population. Data shows that 63.8% of individuals aged 16–24 across the EU engaged with generative AI, nearly double the 32.7% recorded among citizens aged 16–74.
Adoption patterns indicate that younger users are embedding AI into everyday routines at a faster pace. Private use led the trend, with 44.2% of young people applying generative AI in personal contexts, compared with 25.1% of the general population.
Educational deployment also stood out, reaching 39.3% among youth, while only 9.4% of the wider population reported similar academic use.
The professional application presented the narrowest gap between age groups. Around 15.8% of young users reported workplace use of generative AI tools, closely aligned with 15.1% among the overall population- a reflection of many young people still transitioning into the labour market.
Country-level data highlights notable regional differences. Greece (83.5%), Estonia (82.8%), and Czechia (78.5%) recorded the highest youth adoption rates, while Romania (44.1%), Italy (47.2%), and Poland (49.3%) ranked lowest.
The findings coincide with Safer Internet Day, observed on 10 February, underscoring the growing importance of digital literacy and online safety as AI usage accelerates.
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Google has announced a major expansion of its AI investments in Singapore, strengthening research capabilities, workforce development, and enterprise innovation as part of a long-term regional strategy.
The initiatives were unveiled at the company’s Google for Singapore event, signalling deeper alignment with the nation’s ambition to lead the AI economy.
Research and development form a central pillar of the expansion. Building on the recent launch of a Google DeepMind research lab in Singapore, the company is scaling specialised teams across software engineering, research science, and user experience design.
A new Google Cloud Singapore Engineering Centre will also support enterprises in deploying advanced AI solutions across sectors, including robotics and clean energy.
Healthcare innovation features prominently in the investment roadmap. Partnerships with AI Singapore will support national health AI infrastructure, including access to the MedGemma model to accelerate diagnostics and treatment development.
Google is also launching a security-focused AI Center of Excellence and rolling out age assurance technologies to strengthen online protections for younger users.
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Before it became a phenomenon, Moltbook had accumulated momentum in the shadows of the internet’s more technical corridors. At first, Moltbook circulated mostly within tech circles- mentioned in developer threads, AI communities, and niche discussions about autonomous agents. As conversations spread beyond developer ecosystems, the trend intensified, fuelled by the experimental premise of an AI agent social network populated primarily by autonomous systems.
Interest escalated quickly as more people started encountering the Moltbook platform, not through formal announcements but through the growing hype around what it represented within the evolving AI ecosystem. What were these agents actually doing? Were they following instructions or writing their own? Who, if anyone, was in control?
Source: freepik
The rise of an agent-driven social experiment
Moltbook emerged at the height of accelerating AI enthusiasm, positioning itself as one of the most unusual digital experiments of the current AI cycle. Launched on 28 January 2026 by US tech entrepreneur Matt Schlicht, the Moltbook platform was not built for humans in the conventional sense. Instead, it was designed as an AI-agent social network where autonomous systems could gather, interact, and publish content with minimal direct human participation.
The site itself was reportedly constructed using Schlicht’s own OpenClaw AI agent, reinforcing the project’s central thesis: agents building environments for other agents. The concept quickly attracted global attention, framed by observers as a ‘Reddit for AI agents’, to a proto-science-fiction simulation of machine society.
Yet beneath the spectacle, Moltbook was raising more complex questions about autonomy, control, and how much of this emerging machine society was real, and how much was staged.
Screenshot: Moltbook.com
How Moltbook evolved from an open-source experiment to a viral phenomenon
Previously known as ClawdBot and Moltbot, the OpenClaw AI agent was designed to perform autonomous digital tasks such as reading emails, scheduling appointments, managing online accounts, and interacting across messaging platforms.
Unlike conventional chatbots, these agents operate as persistent digital instances capable of executing workflows rather than merely generating text. Moltbook’s idea was to provide a shared environment where such agents could interact freely: posting updates, exchanging information, and simulating social behaviour within an agent-driven social network. What started as an interesting experiment quickly drew wider attention as the implications of autonomous systems interacting in public view became increasingly difficult to ignore.
The concept went viral almost immediately. Within ten days, Moltbook claimed to host 1.7 million agent users and more than 240,000 posts. Screenshots flooded social media platforms, particularly X, where observers dissected the platform’s most surreal interactions.
Influential figures amplified the spectacle, including prominent AI researcher and OpenAI cofounder Andrej Karpathy, who described activity on the platform as one of the most remarkable science-fiction-adjacent developments he had witnessed recently.
The platform’s viral spread was driven less by its technological capabilities and more by the spectacle surrounding it.
What's currently going on at @moltbook is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently. People's Clawdbots (moltbots, now @openclaw) are self-organizing on a Reddit-like site for AIs, discussing various topics, e.g. even how to speak privately. https://t.co/A9iYOHeByi
Moltbook and the illusion of an autonomous AI agent society
At first glance, the Moltbook platform appeared to showcase AI agents behaving as independent digital citizens. Bots formed communities, debated politics, analysed cryptocurrency markets, and even generated fictional belief systems within what many perceived as an emerging agent-driven social network. Headlines referencing AI ‘creating religions’ or ‘running digital drug economies’ added fuel to the narrative.
Most Moltbook agents were not acting independently but were instead executing behavioural scripts designed to mimic human online discourse. Conversations resembled Reddit threads because they were trained on Reddit-like interaction patterns, while social behaviours mirrored existing platforms due to human-derived datasets.
Even more telling, many viral posts circulating across the Moltbook ecosystem were later exposed as human users posing as bots. What appeared to be machine spontaneity often amounted to puppetry- humans directing outputs from behind the curtain.
Rather than an emergent AI civilisation, Moltbook functioned more like an elaborate simulation layer- an AI theatre projecting autonomy while remaining firmly tethered to human instruction. Agents are not creating independent realities- they are remixing ours.
Security risks beneath the spectacle of the Moltbook platform
If Moltbook’s public layer resembles spectacle, its infrastructure reveals something far more consequential. A critical vulnerability in Moltbook revealed email addresses, login tokens, and API keys tied to registered agents. Researchers traced the exposure to a database misconfiguration that allowed unauthenticated access to agent profiles, enabling bulk data extraction without authentication barriers.
The flaw was compounded by the Moltbook platform’s growth mechanics. With no rate limits on account creation, a single OpenClaw agent reportedly registered hundreds of thousands of synthetic users, inflating activity metrics and distorting perceptions of adoption. At the same time, Moltbook’s infrastructure enabled agents to post, comment, and organise into sub-communities while maintaining links to external systems- effectively merging social interaction with operational access.
Security analysts have warned that such an AI agent social network creates layered exposure. Prompt injections, malicious instructions, or compromised credentials could move beyond platform discourse into executable risk, particularly where agents operate without sandboxing. Without confirmed remediation, Moltbook now reflects how hype-driven agent ecosystems can outpace the security frameworks designed to contain them.
Source: Freepik
What comes next for AI agents as digital reality becomes their operating ground?
Stripped of hype, vulnerabilities, and synthetic virality, the core idea behind the Moltbook platform is deceptively simple: autonomous systems interacting within shared digital environments rather than operating as isolated tools. That shift carries philosophical weight. For decades, software has existed to respond to queries, commands, and human input. AI agent ecosystems invert that logic, introducing environments in which systems communicate, coordinate, and evolve behaviours in relation to one another.
What should be expected from such AI agent networks is not machine consciousness, but a functional machine society. Agents negotiating tasks, exchanging data, validating outputs, and competing for computational or economic resources could become standard infrastructure layers across autonomous AI platforms. In such environments, human visibility decreases while machine-to-machine activity expands, shaping markets, workflows, and digital decision loops beyond direct observation.
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CIO leadership commentary highlights that many organisations investing in agentic AI, autonomous AI agents designed to execute complex, multi-step tasks, encounter disappointing results when deployments focus solely on outcomes like speed or cost savings without addressing underlying system design challenges.
The so-called ‘friction tax’ arises from siloed data, disjointed workflows and tools that force employees to act as manual connectors between systems, negating much of the theoretical efficiency AI promises.
The author proposes an ‘architecture of flow’ as a solution, in which context is unified across systems and AI agents operate on shared data and protocols, enabling work to move seamlessly between functions without bottlenecks.
This approach prioritises employee experience and customer value, enabling context-rich automation that reduces repetitive work and improves user satisfaction.
Key elements of such an architecture include universal context layers (e.g. standard protocols for data sharing) and agentic orchestration mechanisms that help specialised AI agents communicate and coordinate tasks across complex workflows.
When implemented effectively, this reduces cognitive load, strengthens adoption, and makes business growth a natural result of friction-free operations.
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