These models are trained on production signals, process behavior, operational patterns, and engineering context.
@fogoros
-
Industrial Foundation Models Transform Manufacturing Production Environments
By
–
Unlike general AI trained on public text, industrial foundation models encode how manufacturing actually works. Their value comes from relevance to production environments, not just generating text or predictions.
— Lucian Fogoros (@fogoros) 14 avril 2026
Partner content with Cybus. #cybus_iiot pic.twitter.com/VYO0qyXuQ9Unlike general AI trained on public text, industrial foundation models encode how manufacturing actually works. Their value comes from relevance to production environments, not just generating text or predictions.
Partner content with Cybus. #cybus_iiot -
Industrial vibration monitoring evolution: predictions for 2028
By
–
Industrial vibration monitoring is evolving fast. Where do you see this heading by 2028? @IIoT_World @CRudinschi @agentic_factory @mirko_ross @AndrewinContact @DimitriHommel
-
Industrial Sensors Demand Robust Performance Beyond Consumer Standards
By
–
Industrial environments demand performance in electromagnetic interference, temperature extremes, and years of continuous operation without drift. Consumer sensors were never designed for that.
-
Consumer MEMS Not Always Suitable for Industrial Applications
By
–
Some companies reuse MEMS developed for consumer applications expecting they will match industrial customer expectations. Most of the time, they do not.
-
Consumer vs Industrial MEMS Sensors for Factory Applications
By
–
Consumer MEMS repurposed for factories vs purpose-built industrial MEMS sensors.
— Lucian Fogoros (@fogoros) 14 avril 2026
"Consumer and industry is not the same," says Dr. Filipe. The gap: accuracy, long-term stability, signal-to-noise ratio.
Partner content with Tronics Microsystems. #tronics_ai pic.twitter.com/qaL6VrCWt3Consumer MEMS repurposed for factories vs purpose-built industrial MEMS sensors. "Consumer and industry is not the same," says Dr. Filipe. The gap: accuracy, long-term stability, signal-to-noise ratio.
Partner content with Tronics Microsystems. #tronics_ai -
Autonomous Energy Systems Decision Speed Requirements
By
–
What's the fastest decision time your energy systems need to make autonomously? @IIoT_World @CRudinschi @agentic_factory @asokan_telecom @KADGLOBAL @Paul4innovating
-
Sub-second response times critical for safety-critical energy systems
By
–
For safety-critical energy systems where consequences of bad decisions are physical, not computational, sub-second response time isn't optional.
-
Edge Compute Software Infrastructure for Distributed AI Model Deployment
By
–
Edge compute requires not just hardware, but software infrastructure that can support model deployment, monitoring, and updates across distributed sites.
-
Edge Computing Enables Real-Time AI Decisions at Data Source
By
–
Conventional approach: send data to cloud, wait for response. Energy reality: decisions need to happen in under a second. Intelligence has to run where the data originates through edge compute infrastructure. Partner content with @IOTechSystems. #iotechsys_iiot pic.twitter.com/dNxyIWtxdY
— Lucian Fogoros (@fogoros) 14 avril 2026Conventional approach: send data to cloud, wait for response. Energy reality: decisions need to happen in under a second. Intelligence has to run where the data originates through edge compute infrastructure. Partner content with @IOTechSystems
. #iotechsys_iiot