Robotics is moving beyond repetitive factory tasks into more complex working environments.
The real breakthrough won't be a robot that can do everything once. It will be one that can perform useful tasks reliably, safely and at scale.
#Robotics#Engineering
@Josephyala AI can speed up development massively, but you still need to know enough to recognise when it's doing something wrong. Otherwise, you're trusting a system you can't properly evaluate, and that can become a problem very quickly.
@DFintelligence The opportunity is huge, but I wouldn't underestimate how much people still value having control over their decisions. AI should make things easier, not take away the ability to understand or choose what happens next.
@AlmustyFX Open-weight models give developers more room to get creative with how they build. Not every application needs the same setup, and being able to tweak things yourself can make a real difference.
Today's smartphones combine increasingly powerful processors, advanced cameras and dedicated hardware for on-device computing.
The interesting shift is towards doing more directly on the device, with less reliance on remote servers.
@caprioleio What’s strange is how quickly we’re getting used to things that would’ve sounded impossible a few years ago. The pace of progress is moving faster than our ability to process what it actually means.
@AIExplainedHQ The real test of automation isn’t how well it works on a normal day, but what happens when something breaks and no one knows what to do next.
@AlmustyFX Remembering everything isn’t necessarily useful at times. The real challenge is knowing what matters now and what’s worth storing for later.
@satyanadella This is where AI can make a real difference. Taking admin work off people’s plates so they can spend more time with patients is a much better use of the technology than adding AI just for the sake of it.
@Aanuoluwapo_123 I think a lot of products get this wrong. They focus so much on showing what they’ve built that they forget to explain why anyone should care. People will always buy solutions to their problems, not a list of features.
@simplydt Honestly, you can be great at building things and still miss what people actually need. The best builders listen, watch how people use their product, and aren’t afraid to change what they thought was right.
@dom_gag_96 I think the real challenge is that writing was never the hardest part. It’s making sure what’s being written actually makes sense and gets things right. AI can speed things up, but human judgement still matters.
The next generation of connectivity will depend on more than faster download speeds.
Network efficiency, coverage, reliability and the cost of delivering each connection will determine how useful new infrastructure becomes.
#Telecommunications#Technology
Europe's major telecom operators are considering a joint bid for mobile satellite spectrum.
This points to a bigger shift: connectivity is expanding beyond traditional ground infrastructure.
#Telecoms#SatelliteTechnology
A Reuters investigation published today found that a hacker used autonomous AI tools to breach more than 100 company websites.
The bigger issue is the economics of cybercrime. When attacks become cheaper to run at scale, defending against them isn't just a business opportunity.
AI stocks are recovering after last week's sell-off, but the bigger question remains: will the returns justify the infrastructure spending?
The AI opportunity is real. That doesn't mean every investment behind it will pay off.
Tesla has unveiled its next-generation Roadster, featuring SpaceX-developed thrusters for aerial manoeuvres.
The engineering is ambitious, but the real test is turning a bold concept into a production-ready car.
Innovation gets attention. Execution earns trust.
#Tesla#EV
As AI systems become more capable, testing them only after deployment is an increasingly risky strategy.
The industry needs stronger evaluations, clearer accountability and safeguards that keep pace with capability.
Moving fast matters.
@techhdive Agreed. Tools just bridge the gap between what an LLM knows and what it can actually do. The interesting part is that the model doesn't just retrieve information; it can decide when a tool is needed, use the result, and reason from it to produce a more accurate answer.