Our startup, founded in 2020, wanted to understand whether we were building a company to scale or if we were about to mask deeper problems with headcount.
Institutional adoption of real‐world asset tokenization is accelerating as major financial firms rely on specialized RWA infrastructure tools that enable compliant issuance, secure custody, reliable data, and cross‐chain settlement.The post 7 RWA Tools Powering Institutional-Grade Tokenization appeared first on Metaverse Post.
Europe's most powerful rocket carries 32 satellites for Amazon Leo network into spaceEstelle.BronkhorstFri, 02/13/2026 - 12:00 KOUROU - The most powerful version of Europe's Ariane 6 rocket carried 32 satellites into space for the Amazon Leo network, which aims to rival Elon Musk's Starlink.The launch from Europe's spaceport in Kourou, French Guiana, on the northeastern coast of South America, is a first for Amazon Leo.The largest number of satellites ever carried by an Ariane rocket successfully separated and set off toward their final orbit to applause from those following the event live at the control centre."What a day, what a launch!" exclaimed Arianespace CEO David Cavailloles, who said the operation proved the launcher's ability to "carry out the most complex missions"."Amazon, your package has been delivered," French President Emmanuel Macron wrote on X, speaking of a "European success".US firm Amazon, founded by billionaire Jeff Bezos, is the main commercial partner for the Ariane 6 despite the latter being touted as a symbol of European sovereignty in the sector.To take on the 32 satellites, the Ariane 6 was upgraded with four strap-on boosters, instead of the two used on the first five flights.The increased number marks "our largest payload that we have launched to date," Martijn Van Delden, head of commercial development for Europe at Amazon Leo, told AFP. With 175 satellites already in orbit, Amazon Leo aims to expand its constellation to 3,200. Rival Starlink, meanwhile, has nearly 9,400 satellites."We're looking to then increase the payload every time we have a new mission, especially as more powerful boosters come online on Ariane 6," Van Delden said. "Ariane 6 is a perfect launcher for constellations" of satellites, said Arianespace CEO Cavailloles during a press briefing. He said the Amazon launches would help in training for a flagship multi-orbital constellation project of the European Union aimed at ensuring secure and sovereign connectivity, with deployment slated to begin in 2029.
For many enterprises, the first real test of AI is not customer-facing products or flashy automation demos. It is the quiet machinery that runs the organisation itself. Human resources, with its mix of routine workflows, compliance needs, and large volumes of structured data, is emerging as one of the earliest areas where companies are pushing [...]The post How e& is using HR to bring AI into enterprise operations appeared first on AI News.
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As generative AI moves from experimentation into day-to-day operations, many technology leaders are reaching the conclusion that a powerful model by itself does not offer sufficient long-term differentiation. Gartner reports that more than half of organizations are already piloting or running generative AI in production, a shift that has pushed CIOs beyond questions of raw capability and toward a more practical concern: Why do some AI initiatives create lasting value while others stall after a promising pilot?That does not mean great models aren’t critically important or disruptive. It does mean that model capabilities alone are not sufficiently transformative for the business.As foundation models become more capable and widely available, the advantage shifts away from intelligence in the abstract and toward how effectively AI is integrated inside the organization.The companies seeing real impact tend to make deliberate choices about what their AI understands, how it shows up in everyday work and where it gets adopted. Those decisions—more than the choice of model itself—are what determine whether AI creates lasting, proprietary differentiation.Does your AI actually understand your business?Many AI discussions still start with capability. Effective CIOs find it is more productive to start with context.AI systems can generate fluent answers, but without visibility into where data comes from, how current it is or whether it is reliable, those answers quickly lose value. The failure mode is rarely apparent by the time bad data gets to the model. Rather than errors being thrown, the system often produces confidently incorrect responses based on stale or incomplete information.Data provides the context that makes AI work – or not. Foundational models are trained on massive corpuses from the outside world and the internet. Still, they can become easily confused when faced with a company’s internal world of private data, including proprietary concepts and alien terminology.Most organizations already have orchestration in place, generating up-to-date, accurate context as a byproduct. Over time, the operational metadata from orchestration becomes a record of how the business actually runs. For example, when a new table becomes the preferred system of record, or a business rule is changed, the orchestration layer and the systems it operates are configured to make it so. This new knowledge can be inferred by this layer to update the company’s AI context quickly and automatically.When AI is grounded in that reality, it behaves differently. It avoids deprecated sources, surfaces data health issues and produces outputs that are easier to trust. As model capabilities converge, this context layer is becoming one of the clearest sources of differentiation.How it shows up in real workA model by itself is infrastructure. What employees experience is everything wrapped around it.AI tools that gain traction tend to appear in the flow of work rather than pulling users into new destinations. They offer help at the right moment, explain what they are doing and give people clear control over what gets accepted or rejected.Trust comes from design choices that may seem mundane but matter in practice. Audit trails, feedback loops and clear escalation paths all play a role. Without them, even capable systems struggle to build enough trust to move beyond experimentation. This is consistent with Forrester’s argument that sustainable AI hinges on trust (and the operating behaviors that create it).The same model can feel either experimental or essential depending on how thoughtfully the experience is designed.Where adoption really happensDistribution is often framed as a go-to-market concern. For enterprise AI, it is more of an adoption challenge.AI spreads fastest when it meets people where they already work. Email, messaging tools, terminals, development environments and core business systems all act as natural surfaces. When AI appears as a new collaborator inside familiar tools, adoption comes naturally and feels like a welcome addition.This shift boosts productivity precisely because it avoids forcing behavioral change, which often becomes an obstacle in its own right. Employees are not being asked to learn a new system. They are interacting with a new capability inside workflows they already trust. Over time, broad usage also produces strong signals about what works best, allowing teams to refine AI systems based on real demand rather than assumptions.For many organizations, lasting value comes not from individual deployments but from becoming part of the default way work gets done.Where the real edge comes fromModels remain a critical source of capability and differentiation, but they do not deliver value in a vacuum. Their impact depends on how well they are connected to context, wrapped in usable experiences and distributed across real workflows.CIOs are also seeing new forms of model differentiation emerge around speed and specialization. In many use cases, smaller models fine-tuned on proprietary data and deployed close to the workflow outperform larger general-purpose models. They respond faster, cost less to operate and align more closely with specific tasks.Proprietary data is central to this shift. Without it, AI outputs are largely interchangeable. Organizations are increasingly recognizing that their internal metadata represents a significant source of advantage to make sense of their business data, and that fine-tuning and domain adaptation are practical ways to turn that data into something defensible.Just as importantly, these systems need a governance posture that keeps them reliable as they scale — i.e., clarity on responsibilities, traceability and mechanisms to manage risk as usage expands. That’s the core focus of NIST’s AI Risk Management Framework, which is designed to help organizations incorporate “trustworthiness considerations” into the design, development and use of AI systems.What this means for CIOsFor CIOs, the challenge is no longer whether AI works. It’s how to create an advantage tailored to their organization.Organizations seeing sustained impact tend to focus less on individual AI tools and more on integration with the systems around them. Context, experience, distribution and specialization determine whether AI becomes a short-term productivity boost or a durable part of how the business operates.This article is published as part of the Foundry Expert Contributor Network.Want to join?
The transformation of the chapel of the Saint-Joseph convent in Saint-Félicien is founded on a guiding principle: designing with what already exists. The intervention seeks neither to erase nor to stage the heritage, but rather to offer a precise and measured reinterpretation of its architectural qualities in service of new cultural uses. The 342 m2 chapel has been converted into a versatile cultural venue hosting cinema, theatre, and exhibitions. The project forms part of a broader strategy for the requalification of the convent and its gardens, led by the municipality in collaboration with local associations. From the earliest stages, the definition of the program was developed in close partnership with the client, through a process grounded in attentive dialogue, trust, and a strong commitment from the local authority.
Helion Energy achieves plasma at a stunning 150 million degree Celsius, well above the threshold to eventually generate stable fusion power.
TOWANDA — On Feb. 9, the National Telecommunications and Information Administration announced that it has approved the Final Proposal submitted by Pennsylvania Broadband Development Authority under the Broadband Equity Access and Deployment program, unlocking federal funding from the Infrastructure, Investment...
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A bug in Google Messages is causing severe battery drain and overheating on some devices. Here's a temporary fix while you wait for Google to push an update.
Redmi 10 Power Price in India: Redmi has launched its new smartphone Redmi 10 Power in India. The smartphone was launched along with Redmi 10A, on Wednesday, 20 April in India
The premium Alienware m15 R7 and m17 R5 highlight four new Ryzen-powered systems.
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System apps with native call-recording functionality remain unaffected.
The OnePlus Nord 2T India launch may take place soon as the moniker has been spotted on the TDRA certification. The OnePlus Nord 2T's camera details have also been leaked. Here are the expected specifications of the device.
The iPhone 16 is likely to be the first Apple device to skip the camera notch.
Garena Free Fire Max redeem codes are 12-digit codes that are region-specific and can be used by players to gain an edge over rivals in every round of the battle royale game.
We are still not sure that no-charger policy will be limited to Narzo 50A Prime or it will be extended to other Realme budget, mid-segment and premium phones
