Climate tech for software engineers

🌿 As software practitioners, how can we adopt green software practices? Here are the key steps: 1. Awareness: Start by becoming aware of the environmental impact of your software. Understand that your application's overall design and efficiency contribute to its energy consumption. 2. Understanding: Gain a deeper understanding of your code's impact using tools and frameworks. The Software Carbon Intensity (SCI) specification and the Impact Framework from our Green Software Foundation are open-source and provide valuable insights into your software's carbon footprint. Leverage these resources to measure and understand the energy consumption of your applications. 3. Opportunity to Apply: Once you are aware and understand your impact, look for opportunities to apply green software practices. There are two main approaches: -- Optimizing Existing Code/Infrastructure/Architecture: Start with small, impactful changes. For example, improve the efficiency of your current codebase and infrastructure. -- Strategic Replacement: When possible, replace parts of your code with more efficient alternatives. For example, A sidecar implementation in Kubernetes transitioned a portion of code from JavaScript to Rust, achieving a 75% reduction in CPU usage and a 95% reduction in memory usage. This shows how strategic replacements can lead to substantial energy savings. (Link to the use case in comments section) 4. Spread the Word: You have the power to make a difference. Share your knowledge and experiences with your peers. Encourage others to adopt green software practices and raise awareness about the importance of sustainability in software development. By taking these steps, we, as a community of software practitioners, can make a significant impact on reducing the environmental footprint of our software. Let’s inspire each other to adopt green software practices. 🌱💡 #Sustainability #GreenSoftware #EnergyEfficiency #TechInnovation #SCI #OpenSource

🌱 Sustainable Coding: The Hidden Lever for Greener IT When we talk about sustainability in IT, the conversation often centers on data centers, cloud providers, or hardware efficiency. But there’s another lever we don’t talk about enough: the way we write code. Every inefficient query, unnecessary API call, or bloated function consumes compute, storage, and ultimately energy. Scaled across millions of users and systems, the carbon footprint is significant. Here are a few practices that can make coding more sustainable: 💡 Write efficient algorithms - Optimized code not only performs faster but also consumes less power. 💡 Minimize data movement - Reducing unnecessary reads/writes and API calls saves energy at scale. 💡 Leverage cloud-native efficiencies - Auto-scaling, serverless, and containerization prevent idle resource usage. 💡 Monitor energy metrics - Just as we track performance and cost, we should start tracking carbon cost per transaction. 💡 Educate engineering teams - Awareness is the first step toward change. Sustainable coding and IT practices are not just an optimization, they’re an environmental responsibility. 🌍 My belief: The future of software engineering will be measured not only in speed and scalability, but also in sustainability. 👉 I’d love to hear your thoughts: How is your organization approaching sustainability in software engineering and IT?

🤖 Artificial Intelligence for climate? 🌍 Two years ago, all my VC friends were clambering over climate tech as the next frontier of investing. Today, AI has captured the zeitgeist and any company with an "AI integration" of some respect is being given second and third looks. As a climate geek, I have recently been thinking how useful AI can be for tackling climate change. At its core, tackling climate change is a hardware problem, so I am not sure any AI software tool is going to be the silver bullet. But I do think there are some interesting segments that are worth keeping an eye on: 🌞 Optimising renewable energy - AI can predict the best times for solar and wind energy generation, improving efficiency and reducing waste. This means cleaner, cheaper energy at a lower cost. 🏭 Smart grids & energy management - AI-driven smart grids can balance supply and demand in real time, reducing energy waste and integrating renewable sources more effectively into our power systems. 👨🌾 Precision agriculture - AI could help farmers monitor crop health, predict weather patterns, and optimise water and fertiliser use. This could lead to higher yields with fewer resources, reducing agriculture’s carbon footprint. ⚛ Carbon emissions tracking - AI can analyse vast amounts of data to monitor and predict carbon emissions at a granular level, helping companies and governments target their reduction efforts more effectively. 🌍 Climate modelling - Advanced AI models can simulate future climate scenarios with greater accuracy, giving policymakers the insights they need to craft effective strategies for mitigation and adaptation. 📃 Carbon credit certification - AI can help build efficiencies in the carbon credit creation process, whether it be helping with carbon accounting or the creation of project design documentation. These efficiencies could speed up the route to market for carbon project developers. I am still figuring out how to integrate AI tools into my own day-to-day. Alongside the odd bit of ChatGPT I am also using FyxerAI which saves me probably about 30 minutes a day sorting through my emails. Interested to hear more companies come to the fore zooming in on the AI/Climate crossover. #AI #Climate