Engineering

This image of Intel’s 18A transistors is worth sitting with for a moment. 📸 What you’re seeing is the real nanoscopic architecture of a chip, resolved down to layers only a few atoms thick by TechInsights. 🤓 That stack of bright horizontal bars in the channel: those are RibbonFET, Intel's name for gate-all-around nanosheet transistors. Made with ASML's EUV lithography. Each ribbon is a thin sliver of silicon that carries current, and the gate material wraps completely around all four sides. That's the real leap from the FinFET era, where the gate could only grip the channel on three sides. Surround it entirely and you get tighter control over the electrons. They switch on and off more crisply, and leak less when they're meant to be off. ⚡️ Now shift your eyes to those darker lines running alongside the transistors. Those are PowerVia, Intel's backside power delivery solution. Historically, every wire in the integrated circuit crowded in from the top of the chip and fought over the same real estate. Here the entire power network has been moved to the back of the wafer (backside power delivery), so signal routing lives on one side and the power rails on the other. You can see how that works on the greyish image. Pulling it off means the finished device is flipped and bonded top-down to a carrier substrate, then thinned and wired from below. A work of art! Put it all together and this single image is quietly showing off several of the hardest problems in modern chipmaking solved at once. And it’s marked simply "Intel" on the full die. 🫳🎤 Source: TechInsights

What happens to a renewables-powered grid when El Niño changes the weather? With update from NOAA: National Oceanic & Atmospheric Administration's Climate Prediction Center, this question is no longer hypothetical. The ENSO Alert System is on El Niño Watch, and the numbers have tightened sharply: 🌡️ 82% chance of El Niño emerging in May–July 2026 🌡️ 96% chance of El Niño continuing through December 2026–February 2027 🌡️ Equatorial subsurface temperatures in the Pacific have risen for six consecutive months Peak strength remains uncertain, but the direction of travel is clear. El Niño doesn't just warm the Pacific. It redistributes rainfall, shifts wind patterns and alters solar radiation across continents. For an electricity system increasingly built on weather-dependent generation, that has direct, measurable consequences: ⚡ Hydropower under stress. During the 2023–2024 El Niño, the Kariba Dam — which powers most of Zambia and Zimbabwe — saw output collapse as the reservoir hit record lows. In Colombia, 80%+ hydro-dependent, even mild El Niño years have reduced reservoir inflows by ~14%. Sudan and Namibia also saw sharp drops in hydropower availability. ⚡ Wind and solar shift, not stop. Wind speeds weaken across parts of South Asia and the Caribbean during El Niño; solar radiation increases in some drought-affected regions and decreases under shifted monsoon patterns elsewhere. The pattern is regional, not uniform — which is why granular forecasts matter. ⚡ Demand spikes. Hotter conditions surge cooling demand across South and Southeast Asia, Central America and the southern US, often at exactly the moment when supply is squeezed. The WMO–IRENA 2024 Year in Review shows that ECMWF seasonal forecasts can now successfully anticipate regional anomalies in solar energy potential and electricity demand months in advance. That is a genuine operational breakthrough for grid planners, traders and policymakers. https://lnkd.in/epwvHkhY Research on Latin America has shown that strategically combining wind, solar and hydro — exploiting their natural geographic and temporal complementarity — can reduce renewable power variability by up to 65% and increase the minimum monthly threshold of available renewable power by up to 5x, including during El Niño droughts. This is where World Meteorological Organization's energy-meteorological services meet energy security. And it is exactly the kind of work that will define whether the clean energy transition holds up under the stress tests climate change keeps sending its way. NOAA CPC ENSO Diagnostic Discussion (14 May 2026): https://lnkd.in/ez4REFDT

What does it actually take to put GLP-1 in a pill?   The headlines make it sound simple – scientists figured out how to turn an injection into a tablet. But the reality behind this breakthrough tells a very different story.   To develop our oral GLP-1 pill for obesity treatment, we tested 217 different molecules. Only one worked.   To solve the challenge of protecting GLP-1 from stomach acid – so it could remain active for a full week in pill form – we tested 70 different research technologies. We finally found success with one called SNAC.   We could have decided to go an easier way, but that would never have led to an “injection in a pill”. We could have compromised, but that would not be us.    This is the reality of pharmaceutical innovation. It requires being relentless in the pursuit of solutions, resilient through countless setbacks, and persistent when others might give up.   That's what 30 years of pioneering research has taught us: breakthrough science requires breakthrough persistence.

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

I dug into the source code of this viral map of global energy infrastructure. Here's what I found. ⬇️ Brian Bartholomew (mention not working for some reason) built OpenGridWorks an interactive map unifying the world's entire power grid into one free explorable platform. 120K+ power plants. 2.7M line-miles of transmission. 800K+ substations. The rendering stack uses MapLibre GL JS with PM Tiles meaning the entire map loads fast, runs client-side, and handles massive vector tile datasets without slowing down. Brian stitched together over a dozen public sources into a single unified layer: EIA Form 860m for US power plant data HIFLD for transmission and substation geometry Global Energy Monitor for global plant tracking OpenStreetMap for base infrastructure WRI Aqueduct for water stress context Epoch AI for compute and energy demand trends IM3/PNNL for climate-energy modeling PeeringDB and TeleGeography for data center and network mapping AESO, USGS, US Census, NREL/AFDC, ITU, and IRS energy community data Plus OpenGridWorks' own derived datasets tying it all together. It's a full geospatial data integration project built on public data alone. Amazing work showing the energy infrastructure that keeps the world (and yes, AI) moving. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing (link in comments)  📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

Fortescue is rapidly electrifying its mining fleet – and starting to displace diesel at scale. It has just added a 15th electric excavator to its fleet, with each unit saving the company 1 million litres of diesel every year. This is what decarbonising heavy industry looks like – not pilot projects, but operational equipment displacing large volumes of fossil fuels. And the economics already stack up: ✅ Fuel is a major operating cost → replacing diesel with electricity reduces ongoing expenditure ✅ High utilisation → equipment runs for long hours, improving the economics of electrification ✅ Dedicated renewable supply → mines are increasingly powered by wind, solar and storage, reducing exposure to diesel price volatility These excavators are tethered by an electric cord to Fortescue's power supply, a practical solution for high-power, continuous operation today. And that power supply is shifting. Traditionally diesel, it is now being rapidly replaced by renewables and storage. Fuel and energy are both Fortescue's largest operating cost and largest source of emissions. Electrifying equipment like this tackles both at once. This is also how Fortescue plans to reach its 2030 “real zero” target – by systematically replacing diesel across its operations. Mining is one of the most energy-intensive sectors in the economy. When electrification works here, the impact is disproportionate. And it doesn’t happen all at once. It starts with individual machines, where the economics and operational constraints align – and then scales across the system.

NEW ANALYSIS: Electric vehicles are entering the mid-transition space starting to replace ICE vehicles in more and more markets. The transition is already underway. Global EV numbers have grown from 1.2 million in 2015 to nearly 60 million today. History shows that shifts like this can happen faster than expected: in the early 20th-century US, horses and mules virtually vanished from roads in under 30 years. As with the rise of the car, today’s transition is shaped as much by policy and politics as by technology. ICE vehicles didn’t dominate through technical superiority alone—they were supported by massive public investment in roads, urban design, and highways funded by fuel taxes. EVs are well placed to move even faster. They directly replace ICE vehicles while being cleaner, cheaper, and quieter to operate. And past transitions suggest that like-for-like replacements—think black-and-white to colour TV—tend to spread far more quickly than entirely new products. Our new report by the Centre for Net Zero (Octopus Energy Group)'s excellent Andy Hackett, Izzy Woolgar, RMI's Yuki Numata and Laurens Speelman and me at Environmental Change Institute (ECI), University of Oxford describe how EVs are posed to enter a next phase in it's adoption curve. This is the phase of 'system integration', where integration of EVs into the broader energy and transport system (think vehicle to grid, flexible charging, widespread and equitable charging, battery recycling) becomes more and more important, alongside reducing costs, intense competition, increasing quality and efficiency, and increasing supporting technologies. This new phase represents new opportunities and new challenges both for policy makers and business which we unpack in this report. You can read the report here: https://lnkd.in/eRNdpMj6

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

Platform product management is an undervalued role in tech. While everyone's chasing the next customer-facing feature, companies are struggling to scale because their technical foundations are falling apart. I've watched organizations spend millions building platforms that nobody uses, simply because they treated platform PM like an afterthought. Most people get this wrong. Platform product managers are the bridge between business strategy and scalable infrastructure. Your customers are internal teams, but your impact reaches every external user. The best platform PMs I know spend just as much time understanding business objectives as they do diving into API architecture. They partner closely with customer-facing product teams to anticipate what the technical foundation needs to support. They think about scalability, developer workflows, and system reliability as strategic advantages. This is one of the biggest skill gaps I see in product organizations today. Companies are forming multiple squads, growing their engineering teams, but they don't have anyone who can translate business growth into platform requirements. If you're a technical PM considering this path, now's the time. Platform demand is growing rapidly, and great platform PMs are in high demand.

No words. No narration. Just one of the most powerful demonstrations of automotive safety technology I’ve seen. This video showcases Volkswagen’s Emergency Assist system, and it’s a masterclass in effective communication. Watch what happens when the system detects an unresponsive driver: 1. Alerts: It first tries to get the driver's attention. 2. Physical Intervention: It then does something fascinating...it jabs the brakes in an attempt to physically jolt the driver awake. 3. Autonomous Action: When there's no response, it takes control. It assesses the surroundings, activates the turn signal, and safely navigates the car across multiple lanes to the shoulder. 4. Secure & Call: Finally, it brings the vehicle to a complete stop, turns on the hazard lights, and automatically makes an emergency call. Think of the real-world implications. It's a lifeline for anyone experiencing a sudden medical event—a seizure, a heart attack, a narcoleptic episode. It also provides a critical safety layer for our aging population, helping older drivers maintain their mobility and independence with more confidence and peace of mind for their families. This technology will save lives. Period. Incredible work by the engineering and design teams at Volkswagen. This is human-centered technology at its best. What are your thoughts? #AutomotiveTechnology #RoadSafety #Innovation #Volkswagen #DriverAssist #Engineering #FutureOfMobility