This space works best when you run the conversation. My role is to guide, support, and unblock you – not to broadcast at you. Here’s how to make the most of it. 1. Start the conversations you wish existed - Post your questions, challenges, and wins – even if they feel “small” - If you’re stuck (paper, career, project, idea), share context + a specific question - Think: “If someone else posted this, would it help me?” – if yes, hit post 2. Use my time intentionally You can access me best by: - Posting in the feed with a clear title and tag (e.g. “Publishing help”, “Career decision") - Tagging me when you want direct feedback or a second brain on something - Bringing concrete things: draft abstracts, LinkedIn posts, reviewer responses, research ideas, career decisions The clearer your ask, the more value I can give in less time. 3. Help each other (this is huge) - Reply to at least one post per week – even with a short thought or question - Share what has worked for you, not just what you’re struggling with - Treat this as a “lab group without borders”: we all get better when we think together If you only consume, you’ll learn something; if you contribute, you’ll learn much more.That's why I do this community! 4. Share your progress publicly - Post quick updates: “Today I…”, “This week I…”, “I finally…” - Celebrate small wins (finished a draft, submitted a paper, survived a review, posted on LinkedIn) - Reflect briefly: “What I learned from this…” – this helps others and cements your own learning 5. Simple norms to keep this valuable - Be specific, be kind, be honest - No “perfect posts” needed – rough and real is fine - Assume everyone here is busy and trying – respond the way you’d want others to respond to you If you’re not sure what to post first, try this: “Here’s where I am right now + the one thing I’m stuck on + the one thing I want from this community.” What’s one post you could make today that would immediately make this community more useful for you and for someone else?

Hi Community, I would like to test a talk topic with you for an upcoming event later this year. The deadline for the Call for Papers (CFP) closes tomorrow: https://compute.events/paris2026/cfp.html How does the proposal read to you? Is there an angle I missed, and would you want to spend 30 minutes listening to this topic? As with all abstract submissions, it needs to be short and to the point to get the message across to a reviewer in 30 seconds. Your feedback would be incredibly valuable in helping me tweak the text and land an invitation to speak! Thanks in advance for your thoughts and feedback! Gijs ============================================ Title: Navigating the Vanguard: A Practical Guide to Selecting Geospatial Foundation Models Brief Summary (Abstract) The recent explosion of Earth Observation Foundation Models (EO-FMs), such as AlphaEarth, TerraMind, and AnySat, has created unprecedented capabilities, but also severe "model fatigue". This talk provides a practical guide for data practitioners to make model selection easier and more transparent. We will map the current landscape of GeoAI models, demonstrate how to evaluate their predictive power using accessible open-source tools, and share a modular pipeline architecture to scale processing on standard cloud GPUs without requiring a high-performance computing (HPC) cluster (for testing the models' local accuracy). Finally, we will unpack the critical "semantic and temporal cautions" to ensure attendees understand the hidden uncertainties in their geospatial embeddings before making operational decisions, because all models are built for a specific purpose, and while they are called “Foundation Models”, there is not one model that fits all use cases - project domains are often very specific, and will need special tuned models to produce a solution. Description Objective & Central Thesis: The geospatial domain is currently experiencing a vanguard of multi-modal, general-purpose foundation models. The central thesis of this talk is that successfully navigating this landscape requires a structured approach to selecting and evaluating models, rather than treating them as magic black boxes. This practical guide aims to simplify model selection while emphasising scalable compute and rigorous uncertainty quantification.

If you've followed me for some time, you've probably noted that I'm very active on LinkedIn. Here's my profile if you'd like to see what I do there. The majority of my network now is in academia & research, as well as consultants & CEOs of companies relevant to my research. The issue is that if you want to get decent visibility on LinkedIn, as any other social media platform, it pushes you to niche down and focus your profile ideally on a single area. This means that: - If I focus my content solely on academic publishing and careers, then my commercial network won't find this content helpful; - If I share my net-zero/decarbonisation research from time to time, it won't get the visibility it deserves. You can see this isn't ideal. So I decided to 'spin-off' my expertise as a research group page. You can check it out here -> Prof Hanak Net Zero Research & Consulting. This is basically a company page that I will use to share my net zero research and run events. I don't expect this to grow to 60k followers - but it doesn't need to be. Its sole role is to be a business-facing page for my research that, I expect, will result in collaborative projects and commercial opportunities. Have you considered doing this for your research expertise? If not, would you like me to run a session on how to build that research profile from scratch?

You paste in your drafts and ask it to "make it better." That's not using AI. That's outsourcing your thinking (and slowly handing your job to a machine.) Here's how I think about it differently: AI is my writing advisor and sounding board I can talk to 24/7. Not my ghostwriter. When I use AI in my writing, I'm not asking it to replace what I do. I'm using it to: → challenge my arguments before a reviewer does → pressure-test my logic when I'm too close to the work → help me see my own weaknesses faster The goal is to come out of that process with better quality output, not to produce something I couldn't have written myself. Here's the uncomfortable truth: If AI can fully replicate your academic voice, your reasoning, your expertise... what exactly are you bringing to the table? Your unique value isn't your ability to write sentences. It's the decades of domain knowledge, the hard-won intuition, the ability to ask questions no one else is asking. AI should amplify that. Not replace it. Now, as a lifelong learner, I'm curious how you are using AI in your academic writing or research workflow?

You don’t need a full content strategy or perfect branding to start sharing your research. You just need one clear message, one simple post, and the courage to hit “publish” even if it feels imperfect. Today, pick ONE recent output (paper, slide, figure, or review comment) and turn it into a 3‑line post: 1. What problem you tackled 2. One practical insight 3. Who this matters for (and why) Then share it on LinkedIn, ResearchGate or here in the community. You’re not “self‑promoting” — you’re making your work easier to find for the people who need it. What’s the one insight you could turn into a 3‑line post today? Drop your answer below 👇 Talk soon, Dawid P.S. If you want, paste your 3‑line draft in the comments and I’ll give you feedback.