Nuno Edgar Nunes Fernandes (Engineering)

Physics Engineer. Engineering, Technology and Science. Global outlook. Solutions provider. Creativity is my passion.

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Capital Follows the Photon iPronics, Oxxius, and the Numbers Behind the Shift from Copper , by @NunoEdgarFern https://t.co/JKZv5iEMkf

[NVIDIA Robotics @NVIDIARobotics]

🦆 Put AI agents to work to help your robot learn to navigate.

Using Microduck as the reference robot, this walkthrough shows how to create a simulated obstacle course with @OpenAI GPT-6 Astra, build 3D assets with @nvidiaomniverse libraries, simulate physics with Newton, train policies in NVIDIA Isaac Lab, and validate them in Isaac Sim on @NVIDIARTXSpark.

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

https://t.co/pvO6Ma3rlm

[Anthropic @AnthropicAI]

New on our Frontier Red Team blog: using Claude to formally verify software at large scale.

Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases (or required a lot of expert time).

With AI models, however, this is beginning to change. In collaboration with @theoremlabs, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span of a few weeks.

Read our research in full here: https://t.co/zUyx8CjBEk

[SpaceQ @CanadaInSpace]

C-COM Q-band antenna tracks stratospheric cell platforms 📡

Ottawa's C-COM Satellite Systems has developed a vehicle-mounted Q-band antenna that follows communications platforms flying about 20 km up 🛰️

🎈 High-altitude platform stations (HAPS) act as airborne cell sites, and each one needs a ground gateway to reach the terrestrial network. C-COM's iNetVu 1200+ puts that gateway on a vehicle.

👉 Read the full story on SpaceQ: https://t.co/p4RJ3YK2mT

#SpaceQ #HAPS #SatelliteCommunications #CanadianSpace #NonTerrestrialNetworks

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Hamamatsu Announces Hikari: AI Driven Support for Photonics Product Selection - Novus Light Today https://t.co/3vDFUXmfJM

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Keys From Orbit. QKDSat takes shape. Plus Roman’s Mirrors and a Moon Lander Under Test., by @NunoEngineering #EWCSpaceDeltaV https://t.co/YFJqA7BbtQ

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

https://t.co/e9x88o9A16

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Opportunity: NASA Rover Completes Mars Mission https://t.co/rc7Ca9K4oW via @YouTube

[EPIC Photonics @EPIC_photonics]

📣 Photon Design (@photond) updates its HAROLD QD quantum-dot laser simulator, cutting runtime by more than half and improving precision on standard laptops or workstations without cloud processing. https://t.co/QktafnTK6o #EPICMemberNews #Photonics #QuantumDots

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Two Couplers and a Length Difference: Building a Mach-Zehnder Interferometer in PhotonForge , by @NunoEdgarFern https://t.co/aGNgrymbBF

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Siemens to digitalize one of Europe’s largest green hydrogen plants @eeNewsEurope https://t.co/xWkCjiV7vk

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Practical MOSFET Electronics: From Theory to Working Circuits
https://t.co/mujV1cQ4b3

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

The Co-Innovation Model in Practice https://t.co/vQUs5lnC6o

[Anthropic @AnthropicAI]

New on our Engineering blog: Creating in-house source control.

In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.

Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: https://t.co/wzNkwHs7IN

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Buying Orbit Early , by @NunoEngineering #EWCSpaceDeltaV #BusinessDeepDive https://t.co/bhmFYCMfHa

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Light Is the Infrastructure: Space Solar Power Takes Its First Power Purchase Agreement, NASA and DOE Put the Reactor Path on Paper, and a CubeSat Laser Goes Routine , by @NunoEngineering #EWCSpaceDeltaV https://t.co/WJde63F4Km

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Three Bottlenecks, One Missing Layer: Where photonic design software meets the AI data-centre supply chain , by @NunoEdgarFern https://t.co/Jr6MdHPacj

[Alex Imas @alexolegimas]

New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery".

While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a la Griliches--e.g., the microscope or statistical inference--which would unlock questions and whole modes of discovery that were previously beyond human reach.

Today, LLMs and specialized models like AlphaFold act as economic complements. LLMs handle analysis, coding, and writing, while specialized tools handle domain-specific predictions. Most handoffs between them run through the scientist.

What may the future of science look like? LLMs orchestrating those handoffs automatically--prompting specialized models, auditing outputs, and looping until either the question is answered or a non-automated stage is reached (e.g., wet lab testing). The scientist's role shifts from running each step to designing this loop. We saw a glimpse of this workflow with Anthropic’s enzyme discovery a few weeks ago, and we're seeing this in our own work too.

This raises foundational epistemic questions:

1. What will scientific understanding look like when discoveries are made by black-box models whose output is increasingly difficult to interpret?
2. How do we extract underlying mechanisms, not just outputs, as science becomes more automated? What will theory look like?
3. If AI automates the writing and junior lab work, how do we train the next generation of scientists to push the frontier?
4. What will motivate scientists in this epistemic reality?

There's an economics angle too. Hypothesis generation gets cheap, so bottlenecks move downstream to verification and to choosing which questions are worth asking. Realizing AI’s full potential will take investment in infrastructure and institutional reform--making this as much an organizational challenge as a technical one

We don't have the answers yet, and I'd love to hear what others think.

Paper here: https://t.co/kvHCFMIFmL

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

The Y Splitter: Our First Walk Through the PhotonForge Documentation , by @NunoEdgarFern #PhotonForge #PrecisionwithLight #Flexcompute https://t.co/nP1oi0Vpwv

[Nuno Edgar Nunes Fernandes (Engineering) @NunoEngineering]

Optical AI chip startup CScale emerges from stealth with $145M funding https://t.co/mx40L9NC8J via @TFNBreakingNews