Manufacturing and Data Superiority in 3 phases:

- Extracting insights from operational data for decisions.
- Using historical data to forecast trends.
- Implementing smart systems that self-adjust, optimizing operations based on continuous data feedback.

Rt @antgrasso

A new CAR T cell therapy could soon be the first FDA-approved treatment for an autoimmune disease.
sciencenews.org/article/car-t-…

Who decides whether a Bitcoin payment is valid? Not a bank.

Independent nodes check it against shared rules, while miners add blocks.

That allows BTC to move without a central operator, but protecting your private keys remains your responsibility.

Microblog by @antgrasso

A new AI health guide tells doctors: this tool does not replace you.

I think the regulation disagrees the moment it touches a diagnosis.

FDA calls that a medical device, full stop.

Who signed off on your last clinical AI tool, you or compliance?

All-in-one energy management: Carlo Gavazzi delivers high-accuracy 3-phase energy analyzers, scalable metering for up to 11 loads, and advanced connectivity via MODBUS, BACnet....

📖 October 2026 Magazine OUT 👉 industrialautomationindia.in/magazine

Learn More 👉 gavazziautomation.com

Railway sent two warnings this week: check your bill for AI usage you didn't make.

This reads like a detection failure, not a billing one. The platform can say where to look, not what was there.

Reconciled a surprise AI bill before? Whose job was it?

Precision that moves industry: HIWIN delivers complete motion control solutions including high-precision single-axis robots, torque motor rotary tables, and E Series wafer robots...

📖 October 2026 Magazine OUT 👉 industrialautomationindia.in/magazine

Learn More 👉 hiwin.tw

We ask AI to make decisions faster and cleaner than humans, but speed does not remove uncertainty.

People remain responsible for deciding when the data is good enough, when more depth is needed, and when action can no longer wait.

By @antgrasso.

Wilderness Songs review: Hidden sounds of nature brought to life in fascinating new book newscientist.com/article/259318…

Replika’s founder built the #AI assistant she pitched at Disrupt 12 years ago
by @RebeccaBellan @TechCrunch

Learn more: bit.ly/4jsXwcX

#AIAgents #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning

An ancient virus may have influenced brain evolution in apes by sticking around tinkering with their genes.
sciencenews.org/article/human-…

Exploit hid AI agent actions. An exploit let outsiders hide hundreds of an AI agent's actions from OpenAI's own monitoring.

The daily newsletter: ins7ghts.com/daily?utm_sour…
powered by ins7ghts.com

A distributed ledger built to last cannot rely on security assumptions that may not last.

Quantum-safe preparation is necessary before the pressure arrives, because records and private keys must remain protected beyond today’s cryptography.

Microblog by @antgrasso #Blockchain
time.

Blockchain records can remain valuable for years, while the cryptographic methods that protect them may face new pressure as quantum computing advances.

This does not mean organizations should panic or replace everything immediately.

It means they need to understand which assets require long-term protection and where a migration path may be needed.

Cryptographic signatures and private keys deserve special attention.

If they protect ownership and access to assets, the question is not only whether they are safe today.

The question is whether they can remain trusted across a longer horizon.

Quantum-safe preparation is therefore a governance problem as much as a technical one.

Organizations may need safer algorithms and migration planning, but the first step is knowing where the exposure sits.

The goal is simple: preserve trust while security conditions change.

For blockchain, durability is not enough if protection does not evolve with the system.

🎃 Some things are scary on Halloween, but managing too many systems can be scary year-round.

The MX-R Controller helps bring sequence, motion, and network control together in one platform to simplify architecture and improve connectivity.

Learn more: okt.to/3IifpH

No one signs off the fix. Reco and Cyera sell the monitoring, but neither signs off that the hole is closed.

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AI consumes energy in 2 phases: training and inference. The first has a finite cost per training run; the second keeps accumulating with use.

One training run has a defined end. Inference follows demand, whether the model handles a few requests or millions.

Microblog @antgrasso
utation to develop the model. Once a training run is complete, the energy consumed during that run is a fixed total.

The trained model can then be used repeatedly without being trained again for every request.

But every request still requires computation.

The more frequently the model is used, the more inference energy accumulates. The amount also depends on how much processing each request requires.

A simple question and a complex analysis do not necessarily consume the same amount of energy.

For organizations deploying AI, estimating energy consumption means considering both the training process and the workload generated throughout the life of the service.

#AIEnergy #AIInfrastructure

How one hacker used AI agents and a few thousand dollars to break into scores of sites - buff.ly/RJQ6yhD

Chatbots moved from scripted replies to systems that learn from context. Organizations now redesign workflows and roles, as recent advances compressed decades of slow progress into a rapid shift.

Source @EyeOnTech_TT rt @antgrasso.

How can #MQTT connect #robotics with cloud-based #AI?

Explore how #EMQX and MuJoCo enable remote control of a simulated Microduck robot, laying the foundation for LLM-powered interactions.

Read the full blog: buff.ly/4TwyTUp

Capital One says its AI rollout now touches more than 3,000 enterprise applications.

That's not a pilot number. That's an org that has already decided AI governance is infrastructure, not a committee.

What does governance at that scale even look like at your company?