#modular-controllers

#modular-controllers

Our customers, ranging from large enterprises to AI research labs, are no longer just asking for AI features. They need a way to collect high-fidelity, synchronized robot and vision data to train AI models on the same robots they intend to deploy. Our AI Trainer is the industry's first direct lab-to-factory solution for AI model training.

Long-range radio waves can pass through obstacles more easily, which makes them perfect for monitoring expansive factories or outdoor infrastructure. A recent report by Fabrity highlighted that these systems use very little power. This allows sensors to operate for 5 to 10 years on a single battery. Using such tech means you do not have to install expensive wiring across your entire site.

Anthropic is expanding its push into the enterprise market with a new set of "coworker" plug-ins designed to embed its Claude AI directly into tools used by investment bankers, HR teams, and engineers, signaling a shift from standalone assistants toward AI agents that operate inside core business workflows.

The scaling model relies on several predictive factors of the system, including the underlying LLM's intelligence index; the baseline performance of a single agent; the number of agents; number of tools; and coordination metrics. The researchers found there were three dominant effects in the model: tool-coordination trade-off, where tasks requiring many tools perform worse with multi-agent overhead; capability saturation, where adding agents yields diminishing returns when the single-agent baseline performance exceeds a certain threshold; and topology-dependent error amplification, where centralized orchestration reduces error amplification.

Retail point-of-sale systems today offer a wide range of options for peripherals and hardware. Their technical specifications play a major role in selection, and big retailers often choose multiple vendors to reduce a single point of failure. This gives them an advantage to negotiate price or support as well. Technically, these peripherals also require updating with new models and may have new feature sets. This necessitates the redevelopment of point-of-sale applications, increasing development costs.

Siemens has published eight new advisories. The company has released patches and mitigations for high-severity issues in Desigo CC, Sentron Powermanager, Simcenter Femap and Nastran, NX, Sinec NMS, Solid Edge, and Polarion products. A medium-severity flaw has been found in Siveillance Video Management Servers. Exploitation of the vulnerabilities can lead to unauthorized access, XSS, DoS, code execution, and privilege escalation.

Baron traces the origin story back to his time building high-scale systems at Instana (which exited to IBM in 2020), where the reality of "always-on" platforms made one thing obvious: the tooling we rely on is often too low-level, too rigid, and too disconnected from real-world use cases. That gap has only widened as environments have exploded in complexity-more cloud providers, more managed services, more hybrid setups, more internal APIs, and "gillions" of tools stitched together into brittle workflows.

Last year I first started thinking about what the future of programming languages might look like now that agentic engineering is a growing thing. Initially I felt that the enormous corpus of pre-existing code would cement existing languages in place but now I'm starting to think the opposite is true. Here I want to outline my thinking on why we are going to see more new programming languages and why there is quite a bit of space for interesting innovation.

After some investigation, I found that Home Assistant has an integration with Node-RED - a graphical tool for manipulating data and event streams. It could probably satisfy most of my needs. But from time to time I remember that I'm a professional software developer, working with event streams for many years, and for this kind of problem there's nothing better than math (and Scala's type system, which supports it very well).

According to Tamas Cser, Founder and CEO of Functionize, the industry is on the verge of a structural shift. By 2026, development teams will transition from AI copilots to agentic fleets: coordinated groups of specialized AI agents operating semi-autonomously across the entire software lifecycle. In this new paradigm, engineering excellence is measured less by syntactic mastery and more by the ability to orchestrate intelligent systems-delegating, validating, and refining work continuously, at machine speed.

All of the appliances and systems are brand-new: the HVAC, the lighting, the entertainment. Touch screens of various shapes and sizes control this, that, and the other. Rows of programmable buttons sit where traditional light switches would normally be. The kitchen even has outlets designed to rise up from the countertop when you need them, and slide away when you don't.

PF4J expects that your plugin code has a class that extends org.pf4j.Plugin interface. And for running and stopping the plugin, methods start() and stop() of this interface will be called. But our service is expected to have completely pure logic - without any side effects - we need to bring these two worlds together - Java impure plugins start/stop and Scala pure logic.

Matter, the smart home connectivity protocol that revolutionized the IoT world, has done wonders to bridge the interoperability gaps between brands. For various reasons, however, Matter hasn't completely solved the problem of incompatibility in the smart home. IoT company Copilot.cx aims to change that by giving users access to different brands' devices with a single mobile app. Copilot.cx has introduced Copilot Star, a platform that enables manufacturers to builda branded app based on a single framework, connecting smart home devices running on different platforms.

Originally developed by Nest (before the Google acquisition), Thread has existed since 2011. Devised as a power-efficient mesh networking technology for internet-of-things (IoT) products, Thread gathered pace after the 2014 formation of the Thread Group, which develops the technology and drives its adoption as an industry standard. Founding members like ARM, Samsung, Google, and Qualcomm have been joined by Apple, Amazon, and many other big companies over the years.