The White House is drastically shortening the deadline for government agencies and organizations to adopt new quantum-resistant encryption systems that will withstand attacks that use quantum computers, as the federal government seeks to protect decades’ worth of secrets belonging to militaries, banks, governments, and most individuals on Earth.
The executive order, titled Securing the Nation against Advanced Cryptographic Attacks, requires computing systems for “high-value assets” and “high-impact systems” to transition to post-quantum cryptographic key establishment schemes by December 31, 2030, and to quantum-safe digital signature schemes by December 31, 2031.
Heading off a significant threat
The new deadline, which for many organizations is about five years sooner than the previous one, comes on the heels of recent research showing that the resources and cost for building a cryptographically relevant quantum computer are far less than previous consensus estimates. In response, Google, Cloudflare, and other companies recently tightened their timelines for moving off vulnerable systems to 2029.
Kubernetes teams automate deployments without thinking about it. CI/CD pipelines fire dozens of times a day, autoscaling adjusts replicas in the background, rollback is muscle memory. But there is one category of automation where that confidence vanishes: letting a system change CPU and memory requests on a running workload without a human reviewing it first.
And as AI inference lands on Kubernetes at scale, that hesitation is becoming hard to ignore, and increasingly expensive.
Why teams trust automation for change but not for constraint
We surveyed 321 Kubernetes practitioners at enterprise organizations earlier this year. The headline finding is one most practitioners will recognize immediately: 82% report high or complete trust in automated delivery controls. But 71% still require human review before applying resource optimization recommendations. Only 27% allow CPU and memory changes to be auto-applied, even within guardrails.
“Deploying code feels additive… rightsizing feels subtractive because you are removing safety margin from a running service, and the failure mode is fundamentally different.”
Those numbers describe a specific asymmetry. The same engineers who deploy to production dozens of times a day without hesitation slow down the moment automation wants to adjust resource allocation. And the survey data make it clear why. Deploying code feels additive. You are shipping new value, the rollback path is well understood, and if something breaks you usually see it right away. Meanwhile, rightsizing feels subtractive because you are removing safety margin from a running service, and the failure mode is fundamentally different.
As one practitioner in the survey put it: “Automated right-sizing carries a unique risk because it directly impacts the underlying stability of the application runtime. Unlike a code deployment that follows a tested path, resource changes alter the invisible contract between the workload and the scheduler.”
When you change resource requests, you change how Kubernetes schedules, prioritizes, and allocates resources. Those effects are not visible the way a code change is. You can’t trace them through a deployment pipeline. And you might not discover that something went wrong until two weeks later, when a traffic spike hits a threshold that didn’t exist at the old values. By that point, three other things have changed too, and proving causation is nearly impossible. The people responsible for those workloads are the same people who get paged at 2 a.m., and they know this.
Why AI workloads raise the stakes
That trust gap existed before inference workloads showed up. What’s changed is the cost of not closing it.
For a long time, teams could absorb the cost of manual oversight. They knew their workloads, had intuition for where the safe boundaries were, and the inefficiency of over-provisioning was a price worth paying for stability. GPU-accelerated inference workloads change that math. GPU compute is significantly more expensive per hour than CPU. The cost of over-provisioning is no longer a rounding error you can absorb quietly. And the workload behavior is less familiar, as inference jobs are bursty in ways teams haven’t built intuition for, traffic patterns shift as models are updated and usage changes, and the resource dimensions involved differ from what teams have spent years learning to tune.
That unfamiliarity compounds with scale. Rightsizing isn’t a one-lever problem the way horizontal scaling is. It involves, at minimum, CPU and memory requests and potentially limits for both, with four dimensions per workload, multiplied across hundreds or thousands of workloads per cluster. The survey data indicates that manual optimization breaks down at around 250 changes a day. Inference workloads push teams past that threshold faster than anything they’ve managed before, because the resource decisions are more frequent and the cost of getting them wrong is higher.
The economic case for automated rightsizing has never been stronger. The organization’s willingness to delegate hasn’t caught up because teams are being asked to trust automation with workloads they don’t yet have a track record with.
What the survey says about closing the gap
When we asked practitioners what would actually increase their trust in optimization automation, 48% said visibility and transparency into how decisions are made, 25% wanted proven guardrails, and 23% needed instant rollback.
Nobody asked for full manual control and very few asked for blind autonomy. What they described is automation that earns trust in stages, and that’s consistent with how the teams furthest along in their automation journey actually got there. They didn’t start with production. They started with a single namespace in a dev environment, observed the system’s behavior, compared recommendations with outcomes, and gradually expanded the scope. Different environments remained at different levels of automation maturity simultaneously, and that was intentional. Production carried more scrutiny than dev.
CI/CD followed the same curve, and the timeline is easy to forget. Most organizations took years to get from running their first automated pipeline to trusting it with production deploys without manual approval on every commit. Kubernetes resource automation is earlier in that same process, and AI workloads are extending the timeline because teams are building trust from scratch with a workload category that doesn’t yet have a track record.
Why automation design matters as much as capability
Some automation architectures deliver meaningful value only with full delegation. The system needs complete control to function the way it was designed to. That’s a form of forced autonomy, and it creates an adoption problem because it asks for exactly the level of trust that most organizations haven’t built yet. Force generally doesn’t work. Teams that feel pushed into a level of delegation they aren’t comfortable with tend to pull back entirely after the first incident.
The alternative is what I’d describe as adaptive autonomy: designing the system to work at every stage of the trust curve. A team still evaluating gets useful recommendations in read-only mode. A team ready to act but wanting boundaries can run guardrailed execution within limits they define. As confidence grows, the system handles more decisions autonomously while humans manage exceptions. And for environments where the track record supports it, closed-loop optimization runs in the background and becomes boring, which is the goal. Each stage is a legitimate operating mode, not a stepping stone you have to rush through.
That design distinction matters more with AI workloads than it ever did with traditional services, precisely because the trust-building process is starting from zero on workloads where the cost of getting it wrong is highest.
“Trust takes a long time to build and a single production incident to undermine.”
The other piece that makes this sustainable is rollout safety. Trust takes a long time to build and a single production incident to undermine. Start with the workloads showing the most headroom between requests and actual usage. Make changes incrementally, small enough that a bad outcome stays contained. Rollback needs to be fast and tied to the health signals the team already monitors. And start with opt-in, not opt-out. Let the teams willing to go first build a track record that others can look at.
The broader pattern
The 71% figure is sometimes read as resistance to automation. I think it’s a more accurate picture of how operational trust actually forms: conditional, earned over time, and moving at different speeds depending on what’s at stake. AI workloads are raising those stakes significantly, which means the path to trusted automation matters more now than it did when the cost of caution was just some unused CPU.
“Most of what gets written about Kubernetes optimization focuses on tooling capability, and the tooling is capable. The harder problem is the human one.”
Most of what gets written about Kubernetes optimization focuses on tooling capability, and the tooling is capable. The harder problem is the human one. If your team is managing AI inference workloads on Kubernetes and your optimization tooling is sitting in read-only mode, the question worth asking isn’t whether to trust the system. It’s whether the system is designed to let you build that trust gradually, starting where the stakes are low and expanding as the evidence supports it, on workloads where getting it wrong costs more than it ever has before.
The growing use of AI contributed to Oracle laying off 21,000 workers in a year, according to a Securities and Exchange Commission filing on Monday.
In its annual regulatory filing for the fiscal year ending May 31, Oracle said it has 141,000 full-time employees. In its 2025 filing, Oracle said it had 162,000 employees. The reported 12.9 percent reduction followed March reports of mass layoffs at the database management software company.
"[T]he adoption and deployment of AI technologies across our operations have resulted, and may continue to result, in reductions to our workforce," the filing reads.
AI deployment decisions don't exist in isolation. The best environment is the one that delivers the most value for each specific AI workload given cost and governance constraints.
New capabilities that reduce data integration tasks and connect data with AI aid developers while helping the vendor carve out a niche amid a competitive landscape.
CIOs and other IT leaders can gain an edge with machine learning, from customer retention to patient care. Blending ML with other AI elements opens new possibilities.
OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.
This 2017 breakthrough idea transformed AI. The concept of self-attention became the foundation of today’s chatbots. Claude, Gemini, and ChatGPT are all large language models (LLMs), AI systems designed to focus on the matter at hand while filtering out distractions.
The results have been remarkable. From brainstorming recipes to generating code, apps, websites, and content, LLMs are being woven into our lives at breakneck speed.
But now, a City University of New York team and collaborators are asking: How closely does AI self-attention resemble human attention?
It’s not just academic curiosity. AI researchers have long looked to the brain for ideas to improve machine intelligence. In turn, AI models have offered new ways to investigate the brain. Comparing artificial and biological attention could inspire AI that concentrates more like us.
In their study, the team asked multiple chatbots to complete a classic psychology test of attention and cognitive control. Participants are shown the word for a color—such as “red”—written in either the same or a different color than the one the word describes. The challenge is to name the ink color while ignoring the word itself.
On short word lists, the chatbots performed at a high level. But as the tasks grew longer, their focus faltered. Instead of naming the ink color, they increasingly defaulted to reading the word. Under more demanding conditions—ones that also trip up people—their performance nearly collapsed.
The findings suggest today’s AI attention systems are “fundamentally limited,” wrote the authors. They go on to say that adding mechanisms similar to “those in biological attention is crucial for achieving artificial general intelligence.”
Attention, Two Ways
Doomscrolling. YouTube. Dinner plans. Family obligations. A barrage of notifications.
Life sometimes seems like everything, everywhere, all at once. Yet the brain can usually lock onto what matters most and push everything else into the background.
Far from a single, straightforward mechanism, attention emerges from multiple brain regions. According to attention network theory, three networks do most of the heavy lifting.
The alerting network keeps the brain ready for action. The orienting network selects which sights, sounds, smells, and sensations deserve attention. Finally, the executive control network resolves conflicts between competing streams of information, helping direct thoughts and actions toward a goal.
Together, these systems allocate the brain’s limited resources. Touch a hot stove, for example, and your brain immediately shifts attention to the burn over dinner. The food can wait; cooling your hand can’t.
AI works very differently.
Rather than processing language as complete sentences, LLMs break text into smaller units called “tokens.” Attention mechanisms then determine which tokens matter most for generating the next word, sentence, or response.
Self-attention is the key breakthrough behind modern chatbots. For each token, the model weighs and incorporates information from other tokens in a sequence, allowing it to track context across long stretches of text. This mechanism helps AI connect words and ideas, and underpins virtually all frontier LLMs today.
Researchers have since built on the concept. One approach, multi-head attention, runs several attention systems in parallel, with each “head” learning different patterns, such as grammar, syntax, or meaning. Another, cross attention, links information across different chunks of inputs and their outputs, making it especially useful for tasks such as translation and summarization.
But attention comes at a steep computational cost. To make models more efficient, researchers are also exploring sparse attention, which limits how many tokens a model considers at once. Another approach draws on information learned in the past to keep AI “focused.”
Despite the name, AI attention is ultimately a mathematical system. It helps determine what information is relevant in a specific context. But it lacks executive control, the network that keeps humans continuously focused on a goal despite distractions for long periods of time.
Color Blind
To test the limits of AI attention, the team pitted OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet against the Stroop task.
Invented by John Ridley Stroop in 1935, the test measures attention and cognitive control by forcing participants to resolve conflicting information. The challenge is simple: Name the color of a word while ignoring what the word means. In a congruent trial, the word “blue” appears in blue ink. In an incongruent trial, “blue” might appear in red or green, creating a conflict between what the eyes see and what the brain reads.
Humans are consistently slowed down by this interference. Even with practice, the effect remains, suggesting it taps into fundamental mechanisms of executive control.
In the study, the researchers created word lists of varying lengths and difficulty. Some were entirely congruent. Others were fully incongruent. A third set mixed the two conditions.
At first, the AI models excelled. On five-word tests, GPT-4o was over 90 percent accurate across all conditions. But as the number of words increased, performance plummeted. On 40-word incongruent tests, the model’s accuracy fell to roughly 15 percent. Claude showed a similar decline. In mixed-condition tests, both models’ performance nearly collapsed to zero.
“The sharp decline in color-naming accuracy with increasing list length indicates that transformer-based attention mechanisms are vulnerable to scaling demands,” wrote the team.
Perhaps most intriguing, some models correctly recognized they were taking the Stroop test and could even explain its rules. But that apparent awareness did nothing to improve their scores. In other words, a “book smart” understanding of the task wasn’t enough to execute it well.
The study joins a growing effort to borrow psychological tests for research in machine cognition, especially when AI is challenged with complex, dynamic decision-making tasks. Theory of mind tests, for example, let researchers gauge whether a system can track others’ beliefs, emotions, and intentions. Personality tests are helping shape model behavior and reduce sycophancy. And some LLMs are readily solving emotional intelligence tests, which measure how well the algorithms recognize and respond to social cues.
According to the authors, the new results point to a missing ingredient in AI attention: A mechanism similar to the brain’s executive control network, which helps us stick to a task and adapt when priorities change.
Future AI systems could benefit from higher-level executive control that continuously tracks progress toward a goal, detects when attention has drifted, and pulls it back on course, if necessary.
Rather than simply weighing which tokens are most relevant in the moment, a more human-like form of attention could help AI stay focused during complex tasks, such as long conversations, multi-step reasoning problems, or high-stakes use in scientific research and drug discovery.
“The ultimate goal of AI research is to develop artificial general intelligence comparable to human abilities,” wrote the team. “AI systems, like humans, may need to master fundamental attention mechanisms…before achieving the generalized problem-solving abilities characteristic of mature executive functions.”
GPT-5 Pro helped solve a 3-year-old immunology mystery, offering insights into T cell behavior. The breakthrough could support cancer and autoimmune research.
Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating...
Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating tokens for customers. And most sites are capped at a fixed power level provided by a regional provider. Under these conditions, performance per watt becomes a key efficiency metric that directly translates to token costs.
For the past two years, the conversation about AI-assisted software development has been dominated by speed. A new GitLab survey of more than 1,500 developers and technology leaders found that 60% say AI coding ROI has already exceeded expectations, and 78% report their teams are writing and committing code faster since adopting AI tools.
But speed without control is a liability.
Most organizations have pursued agentic engineering by adding AI coding tools on top of their existing infrastructure. Coding agents are delivering speed, but that speed isn’t showing up across the full software lifecycle: Only 21% of respondents report productivity gains beyond code generation itself.
“Speed without control is a liability.”
The infrastructure problem runs deeper. Git backends, toolchains, and governance frameworks were built for human-scale concurrency. Agents operate at machine scale, and that mismatch shows up fast. Platform reliability breaks down with millions of agent sessions hitting the same backend, security exposure widens as agents touch dependencies at scale, and cost overruns mount as agents consume tokens inefficiently on infrastructure that wasn’t built for them.
Agentic adoption outpaced governance
The adoption curve for AI coding tools outpaced the development of required guardrails, with 80% of organizations saying they adopted AI tools faster than they developed policies to govern them, and 82% reporting that AI-generated code risks creating a new form of technical debt that their organizations are not prepared to manage.
In practice, that means platform reliability challenges under agent load, security and compliance exposure that widens as agents touch dependencies at volume, and agents operating with artificial confidence because they lack full context. Only 28% of organizations say their software development lifecycle tools are fully integrated with shared data and workflows, which means most teams are trying to govern agent actions across a toolchain that was never designed for them.
Agentic engineering needs agentic infrastructure
Agentic engineering requires two things: agentic coding and agentic infrastructure. Most organizations have the first but lack the second.
Agentic infrastructure spans four areas: the execution layer, the context layer, the governance layer, and the orchestration layer working together.
The first is machine-scale execution. Git backends, CI/CD pipelines, and deployment systems were designed for human-paced development. In the agentic era, they need to handle millions of agent sessions without breaking. When a production incident occurs, the path from symptom back to origin should take minutes, not days.
The second is context that travels with code. As Bastian Stahmer, Business Owner of Vehicle Software Development Platform at Mercedes-Benz, put it on a panel recently, “An agent can only be as good as the context and semantics fed to it.” A context graph connecting code, work items, pipelines, security findings, and production signals is what makes agents genuinely useful at scale and keeps artificial confidence in check.
“An agent can only be as good as the context and semantics fed to it.”
The third is governance built into the flow. Agent actions need to be tied to an identity, logged against a policy, and provable to a reviewer. Low-risk changes move fast, while higher-risk changes trigger review. For Mercedes, operating under automotive regulatory standards that require full traceability and human accountability, GitLab is the control plane where that accountability lives.
The fourth is orchestration. Execution, context, and governance are only as effective as the system coordinating them. The orchestration layer coordinates agent actions across the full software lifecycle according to the policies teams define, determining which agents run, in what order, and how failures and handoffs are managed. Without it, agentic infrastructure is a set of independent capabilities rather than a working system.
What’s next
The next phase of AI in software will focus less on generating code and more on governing it, according to 85% of respondents. That shift reflects how enterprises are maturing their thinking about AI, from a productivity tool to a foundational capability that needs to be trusted, traced, and maintained at scale.
When governance is built into the platform, speed and control are no longer in tension. Traceability becomes a competitive advantage. Context becomes institutional memory. And the codebase, rather than accumulating invisible risk, becomes an asset that grows more reliable over time.
As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs...
As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs generate tokens sequentially, which can limit GPU utilization and constrain throughput in latency-sensitive serving scenarios. Speculative decoding helps mitigate this bottleneck by using a lightweight model to draft future tokens…
Recent breakthroughs in mathematical research show that AI is transforming the field at a remarkable pace. In an open letter published this month, an international group of mathematicians argue that the field needs to remain a human endeavour.
AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files,...
AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files, and iterate on results. But science isn’t software engineering. There is no test suite that turns green when a hypothesis is correct; discovery is iterative, uncertain, and grounded in the physical world. You can’t take a general coding…
Researchers show that serving AI models with llm-d can boost inference speeds by up to 5 times and double throughput — all while using heterogeneous GPUs.
There’s no substitute for authenticity. In a world of AI, real, genuine human connection matters more than ever. The story of Probook starts as real as it comes.
After decades on the job, John, a New York City police officer, was injured on duty and couldn’t serve on the force any longer. Some time later, he took up power washing to stay busy and tapped his son, George Eliadis, to help alongside him. George worked weekends and summers in high school, then continued to power wash while he pursued the prestigious M&T dual-degree program at the University of Pennsylvania.
George noticed how much logistics got in the way of what his dad loved most: power washing. Years later, George spent time inside TR Miller, a home services business in Illinois that became Probook’s first customer, and saw the same problems at scale. He learned that what slowed his dad down affected millions of electricians, HVAC techs, plumbers and other workers. The administrative load, from assigning jobs to following up on website inquiries, cuts into already-tight margins and hurts both customer and employee satisfaction.
AI was supposed to take all that on, but reality hasn’t lived up to the hype. Operators bought a tool for every task, and ended up with a stack that couldn’t talk to itself. Nothing connected; costs ballooned. Customer experience stayed broken, and no one’s life got any easier.
George realized his dad’s life—and an entire industry—would benefit from better, more connected software. So he and co-founders Lewis Zhang and Ben Cervantez launched Probook, the AI Operating System for home services—built around dispatch, the foundation everything else depends on. It is a single platform that shares context across the entire customer experience, automating from intake to dispatch and beyond, and expanding margins for the businesses that keep America’s homes running. Probook is personal for George, of course—but it’s not only George who takes this work personally. All three founders are the real deal. Their authenticity shines through, and it’s the reason why we at Sequoia wanted to partner with them from the seed round and now in their Series A.
Probook works like this: your water heater goes out, and you call a local plumbing company that runs on the platform. Probook’s AI picks up immediately, already knowing each technician’s experience, availability and distance from your home, along with their close rates and ticket sizes. It assigns the right tech to the job, alerts them, and keeps you in the loop with an ETA. You get fast help; the company runs leaner; its team earns more with less stress.
Since childhood, George, Lewis and Ben have been scrappy and relentlessly curious. George is a near lifelong entrepreneur—between power washing jobs, he sold everything from coins to vinyl records. He attended Penn’s M&T program along with Ben, who was valedictorian and captain of his high school football team, and interned at McKinsey during college before dropping out to join Probook. Lewis started coding when he was 7, earned degrees in Electrical Engineering and Computer Science from Berkeley in two years, and was part of the critical player-server matching team at Roblox. Dedication is what drives the team: to say they show up for customers is an understatement. They care deeply about tradespeople and are obsessed with taking the drudgery out of their work, flying all over the country to onboard and work with new businesses in person. And for those companies, Probook has been nothing short of life-changing.
Probook’s growing team is scaling quickly, with a mission to transform the trades. When the nerve center of home services is streamlined by AI that actually drives outcomes, operators can run better businesses and build better lives for their staff. We are proud to support George, Lewis, Ben and team.
The Linux Foundation on Tuesday declared its intent to launch the Agent Name Service (ANS), an open standard that gives AI agents verifiable identities by tying them to the internet’s domain name system (DNS).
The idea behind the ANS has actually been around for a while. It began as a research paper published in May 2025 by the OWASP GenAI Security Project, written by a group of application-security researchers. Its authors include Ken Huang, the CEO of security consultancy DistributedApps.ai and a co-author of the widely cited OWASP Top 10 for LLM Applications that chronicles the top security risks related to LLMs, and Akram Sheriff, an AI security engineer at Cisco.
ANS is a bit of a redesign of the original idea, which has gone through a few iterations since it was published. The 2025 original described ANS as a “universal directory” — basically a central registry with naming borrowed from DNS. A second version, published as an individual draft at the Internet Engineering Task Force in April, takes this a step further and ties each agent instead to a real domain its operator already controls.
How it would work
The design essentially copies how websites already prove who they are today. An operator demonstrates control of a domain like example.com through ACME, the automated protocol behind Let’s Encrypt, and a registration authority issues the agent a pair of certificates. Every change to the agent’s status, from registration to renewal to revocation, is written to an append-only log. A client checking an agent can choose how much assurance it wants, from a basic certificate check to a tier that also consults the log.
It’s worth noting that the ANS system separates identity from discovery and hands the job of finding agents to other services built on top.
The DNS industry and AI agents
Discovery is actually handled by DNS-AID, a separate discovery standard the foundation took in on May 27. It lets agents publish their endpoints as DNS records so other agents can find them. DNS-AID was originally built by Infoblox, and GoDaddy, which is also involved in ANS, is among its backers.
Agent identity and discovery projects based on DNS aren’t limited to these two Linux Foundation projects, though. Including those two, there are now at least four similar proposals. There is DNSid, for example, a durable-identity scheme from the registry operator Identity Digital, and AID, a minimal discovery draft that came out of the developer community.
Vineeth Sai Narajala, a co-author of ANS now with OWASP, says in the announcement, “we didn’t need to reinvent the wheel, we needed to extend the foundational trust of the internet to a new generation of autonomous technology.”
Not reinventing the wheel also means basing this system on the registrars and certificate authorities that come with it and the trust hierarchy they built, which security researchers have long considered fragile.
Maybe it’s no surprise that many agent identity and discovery solutions are coming out of the domain industry. GoDaddy, after all, registers domains, Identity Digital operates top-level domains, and Infoblox, which backs ANS, sells DNS infrastructure. For all of them, DNS-linked agent identity and discovery extends a (profitable) business they already run.
What about A2A and co.?
As is so often the case, the Linux Foundation is playing host to several alternative systems. Google’s A2A protocol, for example, gives agents a signed “Agent Card” they can publish at a known web address, with an agent registry on its roadmap. Cisco’s AGNTCY ships an agent directory and its own cryptographic identity service. Outside the foundation, Microsoft’s Entra Agent ID and Okta for AI Agents, both generally available since the spring, treat an agent as an identity managed inside the corporate directory, with short-lived tokens that tie each action back to the person who authorized it.
And while Cisco is backing both ANS and AGNTCY, some names are missing here, including major players like Google, Anthropic, Microsoft, and Amazon. Given their outsized role in the agent ecosystem, it’ll be interesting to see if they’ll join in this effort or decide on their own standards (insert obligatory xkcd comic here).
New capabilities address the data sprawl that sometimes stops enterprises from successfully building agents and could help differentiate the vendor from competitors.
Nassour, Berberich and colleagues present a soft robotic hand exoskeleton that restores grasping ability in individuals with severe hand paralysis, enabling meaningful tasks such as feeding. A lightweight textile glove with wrist dorsiflexion and an active opposable thumb increases hand articulations to enable more dexterous grasping.
Login
