Engineering

One third of the world’s total solar capacity was installed in the last 19 months. Global installed solar has now passed 3 TW. The first terawatt took decades to build – the latest one took just over a year and a half. Having said that, SolarPower Europe is expecting a slowdown this year before growth picks up again from next year. It forecasts annual installations to fall to 612 GW in 2026 from a record 664 GW last year. This would be the first annual decline in solar installations in more than two decades. A few things stand out: ✅ The slowdown is a China story. China accounted for 57% of global additions in 2025, but installations there are forecast to fall 24% this year following domestic policy changes. ✅ Outside China, however, deployment continues to broaden. India has become the world's second largest market, installing 45.7 GW in 2025. The number of countries with at least 1 GW of solar has risen from 42 in 2020 to 74 now. ✅ Growth is expected to resume from 2027. By 2030 annual installations could reach 864 GW with the global fleet approaching 7 TW. So while the next terawatt might take slightly longer than 19 months, the underlying direction remains clear: annual solar additions are moving towards 1 TW, while deployment continues to spread across more markets.

Google published a free ~50 page whitepaper on the new SDLC with Vibe Coding & Agentic Engineering! Today I'm sharing a 50-page paper co-authored by me, Shubham Saboo and Dr. Sokratis Kartakis Kartakis, and part of Google's 5-day AI Agents course on Kaggle. It's free and we think you'll find it a useful read. AI compresses implementation from weeks to hours. But requirements, architecture, and verification stay stubbornly human-paced. That asymmetry changes everything. The bottleneck isn't typing anymore. It's spec quality. Vibe coding and agentic engineering aren't different tools. They're different disciplines. The difference isn't whether you use AI. It's how much structure, verification, and human judgment surrounds the output. Casual prompts and accepted-whatever-came-back is vibe coding. Formal specs, automated eval suites, CI gates, and human oversight of architecture is agentic engineering. Both use the same agent. What separates them is the harness. Agent = Model + Harness. There's a temptation to treat model quality as the explanation for everything good and bad about your agent. It's wrong, and it leads to the wrong investments. The model is the engine. The harness - the prompts, tools, rule files, sandboxes, guardrails, orchestration logic, observability - is the car, the road, and the traffic laws. When an agent does something wrong, the first instinct is to blame the model. More often the failure traces back to a missing tool, a vague rule, an absent guardrail, or a context window stuffed with noise. Most agent failures, examined honestly, are configuration failures. Three things I believe will stay true as the tools change: Structure scales, vibes don't. Vibe coding is valid for exploration and prototypes. For software organizations depend on, the discipline of agentic engineering is not optional. AI amplifies your engineering culture. Strong testing practices and clear architectural standards get dramatically more value from AI than teams without them. It's a force multiplier, and it multiplies both your strengths and your weaknesses. The human role is evolving, not diminishing. The builders who understand architecture, define precise specifications, and evaluate output critically are more valuable than ever. The skills that matter are shifting from implementation to judgment - from writing code to designing the systems that produce code. Generation is solved. Verification, judgment, and direction are the new craft. We hope you find the new whitepaper a helpful read! Download the PDF here: https://lnkd.in/gPsGzjPZ #ai #programming #softwareengineering

24/7 solar power is no longer theoretical. It is becoming economic reality in some places now. For years, the biggest criticism of renewables was simple: “What happens when the sun isn’t shining?” The answer is increasingly: battery storage. According to the FT, utility-scale battery storage costs have dropped more than 50% since 2022. That cost curve is now enabling large-scale “always-on” renewable systems that combine solar, wind and storage to deliver power day and night. It is a major shift and only the beginning. Projects in Abu Dhabi and Australia are already demonstrating how renewables can move from intermittent generation to near-continuous energy supply at costs competitive with gas.

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

🪂 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.

The European Central Bank is now making the economic case for decarbonisation. Not as climate policy. As monetary policy. Frank Elderson, ECB board member, argues in the Financial Times that Europe's dependence on imported fossil fuels is a structural threat to price stability (👉 https://lnkd.in/eKWWjKbh). The data is damning: energy price shocks pushed euro area inflation to 10.6% in October 2022. Every geopolitical tremor in the Middle East shows up in European energy bills. And the ECB is caught in an impossible bind: tighten to fight inflation and deepen the slowdown, ease to support growth and entrench inflation. The solution is not better forecasting models or finetuned monetary policy. It is cheaper energy. Spain shows what is possible. Wholesale electricity prices in early 2024 were approximately 40% lower than they would have been had wind and solar generation remained at 2019 levels ( 👉 https://lnkd.in/edXgxh9q). Once the infrastructure is built, the energy itself is virtually free. Volatile global commodity markets simply become less relevant. Elderson is explicit: €660 billion per year in clean energy investment sounds large. But Europe already spends nearly €400 billion annually on fossil fuel imports, money that leaves the continent and buys geopolitical vulnerability. Analysis in the UK shows that for every pound invested in sustainable energy, benefits outweigh costs by a factor of 2.2 to 4.1 ( 👉 https://lnkd.in/emEXVfiw). This is precisely what I argued in my piece for Triodos a few weeks ago: Europe's crisis response has been backwards. We keep treating energy dependence as a shock to manage rather than a structural problem to fix. (👉https://lnkd.in/ehFqA6iY) The ECB cannot decarbonise Europe. What it can do is name the conditions: keep the ETS, mobilise capital toward renewable capacity, strip out fossil fuel subsidies, and stop confusing cheap fossil fuels with affordable energy. If people need help with energy costs, target it: don't suppress the price signal that drives the transition. The cheapest energy is the energy we no longer have to import.

Applicators and Tips are shaping your beauty brand experience. Most professionals underestimate them, but they are the end points of packaging through which the product exits. Be aware... Global applicator tips market is projected to grow from roughly USD 2.4 billion in 2025 to USD 4.5 billion by 2035, at a CAGR of ~6.7 % +72 % consumers say packaging design is a major factor in their purchase decision for beauty and personal care products. >>INNOVATIONS. These innovations are especially critical in premium skincare, where product integrity, hygiene, and consistent application are paramount. Modern applicators go beyond simply guiding the product: +Silicone Valves → Prevent backflow & contamination. +Air-Tight Closures → Preserve sensitive formulations. +Anti-Spill / Leak-Proof → Travel-friendly, mess-free. +Controlled-Flow → Precise dosing & less waste. >>PERCEIVED Value. Consumers link advanced applicators, airless pumps, silicone valves, decorative nozzles, to innovation and premium quality, making products feel more exclusive and appealing even when formulas are similar. +61 % consumers say they are more likely to repurchase beauty products that feel luxurious in their packaging. >>HYGIENE Perception. Features like silicone valves, air-tight closures, and leak-proof tips reassure users that the product is protected from contamination and oxidation. Consumers increasingly associate these features with trustworthiness and quality, which can sway them to purchase or stay loyal to a brand. +70 % purchase decisions are made at the retail shelf, where packaging is the first interaction point. >>EXPERIENTIAL Engagement. Decorative or shaping nozzles add visual and tactile pleasure to application. Creams that form ribbons or stars on the skin create a memorable, “Instagrammable” experience. The experience of using the product becomes a differentiator, influencing repeat purchases and brand advocacy. +Airless/Silicone valve → Hygiene → Builds trust & loyalty. +Decorative/Shaping → Sensory appeal → Enhances luxury perception. +Controlled-flow tip → Easy application → Encourages repeat use. +Multi-chamber/Multi-orifice → Novelty → Sparks curiosity & premium feel. +Leak-proof/Spill-proof → Travel-friendly → Simplifies purchase decision. Conclusion Innovative applicator tips do more than dispense product, they enhance hygiene, ease of use, and sensory appeal, driving purchase decisions, brand loyalty, and premium perception in the beauty market. Find my curated search of examples and get ready for your new Hero!. Featured brands: Beyond Glow Belif Bubble BRTC Elf Kylie Cosmetics Omy Refy Reviderm SUNJUK JEJU Vyru Wipped #beautybusiness #beautyprofessionals #beautypackaging #packagingprofessionals

Stop collecting certifications like Pokémon. Most of them won't get you hired. The right certification depends on your role, not the trend. Here's a roadmap by role and skill level: 𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: Google Data Analytics, IBM Data Analyst, Excel Associate → Intermediate: Power BI PL-300, Tableau Data Analyst, Google BI → Advanced: Microsoft Fabric, AWS Data Analytics Specialty 𝟐. 𝐁𝐈 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: Google BI, Power BI PL-300, Tableau Desktop Specialist → Intermediate: Tableau Data Analyst, Looker Analyst → Advanced: Microsoft Fabric, Tableau Consultant, CBIP 𝟑. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭 → Beginner: Tableau Desktop Specialist, Power BI PL-300 → Intermediate: Tableau Data Analyst, Looker Visualization → Advanced: Tableau Consultant, CDVP 𝟒. 𝐒𝐐𝐋 / 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 → Beginner: SQL Basics (DataCamp/Coursera), IBM SQL for Data Science → Intermediate: Azure DP-900, PostgreSQL Associate → Advanced: Azure DP-300, Snowflake SnowPro Core 𝟓. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 → Beginner: AWS Cloud Practitioner, Azure AZ-900 → Intermediate: Databricks Data Engineer Associate → Advanced: Azure DP-203, Google Professional Data Engineer 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐭𝐞𝐥𝐥𝐬 𝐲𝐨𝐮: You have 6 certificates on your LinkedIn. Zero projects in your portfolio. Guess which one hiring managers actually look at? Certifications open conversations. Projects and portfolios open doors. A certificate says you learned something. A project proves you can do something. Don't chase all of them. Pick based on your domain. Your interest. Your career goals. Then stick with it long enough to build real depth. Depth beats breadth. Every time. What certification are you currently working on? ♻️ Repost if someone in your network needs this roadmap

Leadership across companies expects AI to magically cut time-to-ship in half. Yes, AI can help engineers code faster. But coding was never the bottleneck. The real drag is non-tech stuff - planning, periodic status updates, cross-team coordination, unclear requirements, stakeholder alignment, reviews, approvals, handoffs, deployment cycles, and on-call rotations. Cumulatively, that's ~80% of the work. Optimizing the remaining ~20% cannot produce a 50% reduction. If leaders really want to see gains, they should focus less on coding velocity and more on eliminating process friction.

India’s green economy is growing fast but LinkedIn data suggests green talent is growing even faster. The LinkedIn Hiring Rate (LHR) for green talent — defined as professionals with green skills, green job titles, or both — is now 59.7% higher than for the overall workforce. This means green-skilled professionals are significantly more likely to be hired than their peers, underscoring the growing demand for sustainability-focused roles. “The prioritisation of green talent by Indian companies is being fuelled by an interplay of policy reforms, rising consumer consciousness, and the need for deep business transformation,” says Neelima Burra, Chief Strategy, Transformation, and Marketing Officer at Luminous Power Technologies. “Government initiatives like the PM Suryaghar Yojna, National Solar Mission, and Smart City Mission, combined with the growing mandate for ESG reporting — are also pushing companies to recruit sustainability experts, carbon auditors, and ESG strategists to meet regulatory and investor expectations,” she adds further. Operational efficiency has emerged as the top skill across the top five industries increasingly hiring for green skills, as per LinkedIn data. In contrast, precision agriculture skills lead in farming, ranching, and forestry — highlighting how sector-specific green skills are evolving. “Operational efficiency offers the fastest route to tangible returns. It moves the conversation beyond regulatory compliance to net profitability, ensuring we can do more with less energy and fewer materials,” says Venu Nuguri Managing Director and CEO at Hitachi Energy. This surge in demand aligns with broader economic trends. Green jobs in India have grown over 10 times in the past five years, with Gen Z accounting for 63% of applicants, reports The Economic Times, citing a report by WeNaturalists. The projections are equally ambitious. India’s green economy will generate 7.29 million jobs by FY28 and 35 million by 2047, as the sector scales toward a $1 trillion valuation by 2030 and $15 trillion by 2070, suggests another report by The Economic Times, citing a report by NLB Services. The message is clear: green skills aren’t just good for the planet — they’re becoming essential for employability. As India accelerates its climate and economic goals, the workforce is already adapting. The question now is whether education, training, and policy can keep pace. Read the full report here: https://lnkd.in/g873CzHT #COP30 #GreenerTogether Source: The Economic Times: https://lnkd.in/d-3bShQP  The Economic Times: https://lnkd.in/dSUMFS58