Open Source Innovation Platforms

In many nonprofits, innovation often mirrors privilege. Who gets to dream up solutions? Whose ideas are embraced as “bold” or “innovative”? Too often, decision-making is concentrated in leadership or external consultants, leaving grassroots, community-driven insights underutilized. This perpetuates inequity and stifles transformative potential within our own organizations. Here’s the truth: Privilege shapes perceptions of innovation: Ideas from leadership or external experts are often prioritized, while community-driven ideas are dismissed as “too risky” or “impractical.” Communities with lived experience are sidelined: Those who deeply understand systemic challenges are excluded from shaping the solutions meant to address them. The result? Nonprofits risk replicating the same inequities they aim to dismantle by ignoring the imaginative potential of those closest to the issues. When imagination is confined to decision-makers in positions of power, we limit our ability to create truly transformative solutions. As nonprofit practitioners, we can start shifting this dynamic by fostering equity within our organizations: * Redistribute decision-making power: Engage community members and frontline staff in brainstorming and strategic discussions. Elevate their voices in decision-making processes. * Value lived experience as expertise: Treat the insights of those who experience systemic challenges as central to innovation, not secondary. * Create space for experimentation: Advocate for internal processes that allow for piloting bold, community-driven ideas, even if they challenge traditional approaches. * Focus on capacity-mobilisation: Invest in staff and community partners through training, mentorship, and resources that empower them to lead imaginative projects. * Rethink impact metrics: Develop evaluation systems that prioritize community-defined success over traditional donor-centric metrics. What practices has your organization used to centre community-driven ideas? Share your insights—I’d love to learn from you! Want to hear more: https://lnkd.in/gXp76ssF

Meta went bonkers with this new open-source ASR that works for 1,600+ languages! 🤯 Now, businesses can reach customers in their native tongue, even in low-resource regions, without building ASR from scratch. → Fully open-source, supporting 500+ languages never covered by any ASR before → Trained on 4.3M hours of multilingual speech (1,600+ languages) → Best part: Works zero-shot on languages never seen during training How? Two breakthroughs: Dual-decoder architecture:  • CTC decoder for low-latency, real-time use  • LLM-ASR decoder (Transformer-based) for high-accuracy, context-aware transcription In-context learning: Just 5–10 speech-text examples at inference time, let it transcribe any new language even if the model was never trained on it. Even more surprising: → On FLEURS-81, Omnilingual ASR beats Whisper on 65/81 languages—including 24 of the world’s top 34 most spoken languages → Robust to noise: CER stays <10 even in the noisiest 5% of field recordings → Scales from edge to cloud: 300M (mobile) → 7B (max accuracy) But the real shift isn’t scale, it’s agency. Communities can now extend ASR to their own language with minimal data, compute, or expertise. Check out the carousel to know how it works in simple terms and what the challenges are in detail. Question for you: When building voice tech for underserved languages, do you prioritise zero-shot generalisation or lightweight fine-tuning and why? Follow me, Bhavishya Pandit, for honest takes on AI tools that actually work 🔥 P.S. Model card, inference code, and datasets in the first comment.

Bad ideas don’t kill innovation. Rigid cultures do. Someone proposed a small fix in a routine process. It cut errors, made work smoother, reduced stress. The team adopted it. It worked. But because it didn’t come from the “right level,” it was brushed aside. The change stayed. The credit didn’t. And the person behind it never spoke up again. Not because the idea was wrong, but because the culture wasn’t ready. Change isn’t about more ideas, it’s about reshaping culture itself. So what does that look like in practice? 1️⃣ Reward questions, not just solutions Innovation doesn’t begin with a polished pitch. It begins when someone dares to ask: “Why are we still doing it this way?” 2️⃣ Make failure boring If every test must succeed, no one will try. Normalize small, fast, safe failures , momentum builds without blame. 3️⃣ Remove the need for permission Good ideas die when they need five layers of sign-off. Trust creates velocity. Embedding these into an existing culture is never easy. One way forward is to create safe spaces where people can test, learn, and adapt without red tape. That’s exactly what Design4Impact (D4I) set out to do, not just talk about culture, but build it in practice. D4I launched in 2020, in the middle of COVID-19 to bring health, social, and design for good together. And it grew into a national platform that empowered communities with tools and trust to co-create solutions. In 5 years, D4I: 🔸 Trained 500+ participants in design thinking 🔸 Ran 3 national design challenges 🔸 Launched 9 pilots, from tackling mental health to helping caregivers feel less alone I was blessed to serve on the organizing committee, and to witness firsthand how trust, not hierarchy, unlocked real innovation. The collective wisdom has now been distilled into the D4I Playbook. It isn’t just a handbook. It’s an invitation, to rethink how we innovate, and to design with communities, not for them. 👉 Access it here: https://lnkd.in/ghJ3S6ex Culture isn’t shaped in glossy labs or polished decks. It’s shaped in the messy middle, late nights, side conversations, the meetings no one wants to attend. Every unspoken idea is a missed opportunity, to make things better, faster, or more human. If you’re in a leadership role: Who’s the quiet innovator on your team you need to hear from today? 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡The ongoing series and additional resources are available at www•enabler•xyz 💡Repost if this message resonates with you!

75% of internet users don't speak English. Yet most B2B sites are English-only. And then we wonder why international expansion feels complicated. It’s not complicated. It’s just operationally painful. For years, going global meant: Translator. Developer. SEO specialist. Weeks of coordination. So companies postponed it. But if you’re on WordPress, Shopify, Webflow, Squarespace or almost any CMS… You can make your site multilingual in minutes. That’s what I found interesting about Weglot. Install it once, and your site is instantly translated with AI. Behind the scenes, it also handles: • Language-specific URLs • Hreflang tags • Translated metadata • Automatic updates when content changes In other words, multilingual SEO without turning it into a dev project. You can refine key pages manually, control tone, and manage everything from one dashboard. Which changes the decision entirely. Instead of asking: “Are we ready to expand?” You ask: → “Which market should we test next?” If your website only speaks one language, you are not limiting ambition. You are limiting access. And access is leverage. Want to see how this works in practice? Try it here: https://lnkd.in/enXEbGFS If removing the technical friction made expansion easy… Which country would you test first?

The 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐑𝐨𝐨𝐦 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 (𝐏𝐑𝐨𝐅) teaching case shows how a large healthcare consortium and a small group of manufacturers collaborated to rethink innovation in a highly regulated sector. At its core, the case demonstrates how PRoF turned the interaction between two very different communities into its main innovation engine. The large consortium represents the healthcare user community: nurses, doctors, caregivers, patients, and hospital managers who express the lived reality of care. Their contribution is experiential and value-based. Through structured “brainwave sessions,” they surface latent needs and convert them into broad keywords such as comfort, privacy, dignity, or anti-loneliness. These keywords form a shared language that avoids technical jargon and allows hundreds of users with diverse perspectives to converge around common priorities. The small consortium consists of manufacturers, architects, and designers who have the capabilities to transform these user insights into concrete room concepts. Their commercial goals are kept strictly outside the creative process, allowing trust to grow between the groups. Once the user community defines the keywords, the producer community develops prototypes, after which the large consortium returns to evaluate and refine them. This modular sequencing keeps tensions low, ensures rapid progress, and prevents commercial logic from dominating user needs. The interaction between these two communities solves a longstanding problem in healthcare innovation: suppliers often misunderstand user needs, while users lack the means to innovate. PRoF bridges this gap by letting users drive ideation and letting producers translate that insight into solutions. What emerges is a genuinely user-oriented innovation ecosystem in which neither community could succeed alone, but together they generate concepts that reshape expectations of care design. You can find the case study at HBSP: https://lnkd.in/e6nxTFM7 #UserCentricInnovation #Collaboration #OpenInnovation #CrossCommunityCollaboration #HealthcareEcosystems #CoCreation #Ideation

An ecosystem is more than just integrations! Plugging into APIs and calling it a day is NOT partnerships. Often integrations are table stakes to opening a door towards a partnership, in order to drive growth and win-win's for all participants. If not, they’re just a marketing collaboration which will only go so far. Or worse, something that keeps your team busy without delivering real impact. Partner managers inevitably get fired or leave frustrated sooner or later. This is what happens in most companies: ➡ They integrate where engineering capacity allows without a clear strategy. ➡ They measure success by connection count, or number of partners and not by real business impact. ➡ Their partnerships aren’t aligned with the company’s big-picture goals, and this isn't communicated effectively enough. So what should your team be doing instead? ✅ Assess where ecosystem plays make sense. Not every integration is valuable. Focus on ones that enhance your core offering, outside of your product roadmap, improve customer experience, or create new revenue streams. ✅ Prioritise partners that create network effects, not just one-off connections. The best partnerships amplify your business by driving more adoption, expanding reach, or unlocking new markets. ✅ Structure partnerships for repeatable success. Build systems. An effective ecosystem isn’t built on one-time deals, it’s designed for scalability and long-term value on both sides Aimless integrations are just an expense. Light integrations too often frustrate customers rather than add value. A true ecosystem attracts the right partners, the right users, and creates real business impact along the entire customer journey. If your company needs help building a real ecosystem, we at Hockey Stick Advisory can help. #partnership #ecosystem #growth

How often do we design with people, instead of for them? It’s easy to fall into the trap of thinking that creativity is something only designers hold the key to. But when we pause and engage with communities, we realize something powerful: Creativity thrives within the community itself—it just needs the right conditions to flourish. Take, for example, the Collective Action Toolkit (CAT) by Frog. It’s not just a tool; it’s a framework that empowers communities to solve problems by tapping into their collective strength. Through a series of activities—like clarifying goals and imagining new ideas—small groups around the world have used this toolkit to not only share their thoughts but to take decisive action that addresses their concerns. The beauty of this approach is in its adaptability. It’s not a one-size-fits-all model. Each group can mould it to fit their unique needs, ensuring that everyone’s voice is heard and valued. But collaboration, as we know, isn’t always easy. There’s often discomfort, sometimes even conflict, when differing ideas meet. Yet, as designers, navigating these challenges is where true progress happens. As Otto Scharmer and Peter Senge, leaders in organizational development, have shown, it's in this space of tension that new solutions are born. A recent contribution from @Design Impact offers a set of guiding principles for designers to keep in mind when working with communities. One of these, “Value me for who I am, not who I’m told to be,” resonates deeply. It’s a reminder that behind every design is a real person, with history, emotions, and passions. When we acknowledge that, we move beyond simply gathering feedback—we tap into real leadership within the community. At the end of the day, Social innovation isn’t just about creating a product or service. It’s about co-creating, about building alongside communities rather than handing down solutions. It’s about fostering a space where everyone’s creativity can shine, and where long-term, sustainable change is possible. Have you been part of a design process that values community leadership? What challenges—and opportunities—did you encounter along the way?

Generative AI is transforming how people learn and work—but only if it speaks your language. Most AI features are still built English-first, and “just translate it” rarely delivers a great experience. Idioms, domain-specific terms, and cultural context often get lost when translation is treated as an afterthought. In a recent engineering blog, Udemy shares how they approached this challenge and built a framework for localizing generative AI features from the ground up. The team outlines three strategies along a spectrum of complexity. At one end is a Translation Management System (TMS): translate user input to English, run it through the LLM, then translate the output back. It’s fast to ship and offers broad coverage, but comes with tradeoffs in latency and nuance. At the other end is a Multilingual LLM System (MLS), where the model processes and generates directly in each language using multilingual prompting, cross-lingual embeddings, and optional fine-tuning. This delivers higher quality, but is more complex to build. In between sits a hybrid approach—routing simpler queries through TMS and high-stakes interactions through multilingual models—allowing teams to move fast while investing deeply where it matters most. What stands out is how they treat localization as a platform problem: core interfaces are designed to switch between TMS and MLS without major rewrites. Safety and compliance are validated per language, rather than assumed to generalize from English. And every new language follows a repeatable playbook: start with TMS, learn from real usage, then decide whether it’s worth upgrading to a fully multilingual system. The results are impressive: the team was able to go from concept to production for the Japanese market in under three months, and adding a new language now takes less than 25% of the original effort. The takeaway? Localization isn’t a tax on your AI roadmap—it’s a multiplier. Start broad, go deeper where the data justifies it, and design your system so scaling globally becomes the default. #DataScience #DecisionMaking #LLM #Translation #Platform #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/ggZxBDYj

Switzerland’s Ethical GPT Alternative Is Finally Live 🚀 Built in the Alps. Powered by green energy. Trained in 1,000+ languages. And open to everyone. Apertus, Switzerland’s first open-source GPT-4-scale LLM has officially launched. And it’s rewriting the rules of AI development. While OpenAI, Anthropic, and Google dominate headlines with closed models, Switzerland just quietly released something different: a multilingual, energy-efficient, fully transparent alternative. And they’re giving it away. 🔐 True Digital Independence Not another hype model. A statement: Built by public institutions (ETH Zurich, EPFL) Apache 2.0 licensed — completely open Full access to weights, training data, and recipes No Big Tech lock-in. No black boxes. No strings attached This is AI infrastructure that everyone can inspect, improve, and trust. 🌱 AI Powered by the Alps Apertus was trained using Switzerland’s carbon-neutral supercomputers — running on 100% renewable alpine hydropower. Sustainable AI is not a buzzword here Its energy footprint is radically smaller than GPT-4 and Claude Proving you can build world-class models without draining global energy grids 🌍 AI That Speaks 1,000+ Languages Unlike most commercial models, Apertus was trained, not just fine-tuned, on over 1,000 languages. That means underserved communities finally get native-quality AI: Regional dialects Indigenous languages True multilingual parity AI shouldn’t just work for English speakers. Now, it doesn’t have to. ⚖️ Built With Ethics First, Not After Apertus is EU AI Act-ready from day one: Training data is fully documented Respects copyright and website opt-out signals Privacy isn’t bolted on — it’s baked in Transparency isn’t optional here. It’s the entire point. But Here’s the Catch… Apertus isn’t perfect — yet. It lags GPT-4 and Claude on advanced reasoning, coding, and multi-step problem solving It’s missing some ecosystem perks — no plugins, memory, or multimodal features Running the 70B model requires serious compute; the smaller 8B model is more accessible but less capable Still, for an open model just launching? It’s an impressive foundation — and it’s only going to improve. 💡 Why This Matters Apertus proves something critical: LLMs don’t have to be closed, extractive, or environmentally destructive. This is a blueprint for the future: Public ownership Academic freedom Energy responsibility Global accessibility Open. Transparent. Ethical. Exactly what AI should be. ♻️ If this resonated, share it with your network. 🔔 Follow Alex Issakova for more reflections on AI and leadership. 

“𝗦𝗰𝗮𝗹𝗶𝗻𝗴” 𝗶𝘀 𝗮 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱. But often it causes more harm than good: We hear the word scaling everywhere in the field of social innovation. But: 𝗪𝗵𝗼𝘀𝗲 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗮𝗿𝗲 𝘄𝗲 𝘂𝘀𝗶𝗻𝗴? And: 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗹𝗱𝘃𝗶𝗲𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗿𝗲𝗳𝗹𝗲𝗰𝘁? Dominant models of scaling prioritize: • Metrics over meaning • Speed over stewardship • Reach over relationships They are rooted in 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 𝗩𝗮𝗹𝗹𝗲𝘆 𝗹𝗼𝗴𝗶𝗰. And, 𝗰𝗼𝗹𝗼𝗻𝗶𝗮𝗹 𝗺𝗶𝗻𝗱𝘀𝗲𝘁𝘀. Therefore they treat social change like a tech product. That kind of scaling often does harm: • It erases context. • It sidelines local wisdom. • It centers Global North norms. So… What does 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆-𝗹𝗲𝗱 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 look like? We talked to local innovators across five countries, who are re-imagining scaling. Not as replication. But as 𝘀𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻𝘁𝘆, 𝘀𝗼𝗹𝗶𝗱𝗮𝗿𝗶𝘁𝘆, and 𝘀𝗲𝗹𝗳-𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻. 🌀 𝗜𝗻 𝗚𝘂𝗮𝘁𝗲𝗺𝗮𝗹𝗮, scaling honors 𝘉𝘶𝘦𝘯 𝘝𝘪𝘷𝘪𝘳 — a Mayan worldview of harmony with nature and collective resilience. 🌱 𝗜𝗻 𝗜𝗻𝗱𝗼𝗻𝗲𝘀𝗶𝗮, innovators lean into 𝘨𝘰𝘵𝘰𝘯𝘨 𝘳𝘰𝘺𝘰𝘯𝘨 — mutual aid and interdependence rooted in cultural values. 💪🏽 𝗜𝗻 𝗖𝗮𝗺𝗲𝗿𝗼𝗼𝗻, scaling flows through 𝘕𝘥𝘢𝘯𝘪𝘧𝘰𝘳 — community fellowship, where trust is the true measure of success. 🐝 𝗜𝗻 𝗕𝗿𝗮𝘇𝗶𝗹, the Meli Bees Network spreads not products, but ancestral knowledge, ecological stewardship, and collective care. 📣 𝗜𝗻 𝘁𝗵𝗲 𝗣𝗵𝗶𝗹𝗶𝗽𝗽𝗶𝗻𝗲𝘀, “scaling” doesn’t even translate neatly. Instead innovators use words that hold nuance, such as: 𝘗𝘢𝘨𝘱𝘢𝘱𝘢𝘵𝘶𝘭𝘰𝘺 (sustaining) or 𝘗𝘢𝘨𝘺𝘢𝘣𝘰𝘯𝘨 (growing). Across all five places: 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗞𝗣𝗜. It’s an 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝘁, 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. One that honors: - Autonomy - Cultural identity - Community wellbeing. This report is a call to: - Amplify locally-led change. - Transform systems of coloniality. - Re-imagine innovation in service of life. Gratitude to the 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆-𝗟𝗲𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 (𝗖𝗟𝗜𝗣) for trusting us at Indigenous & Modern to co-create the report. And to the incredible partners who made it real. Special thanks to Joshua Konkankoh & Isabel Gennaro from the I&M team. 📖 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 👉 https://lnkd.in/e-cCMxZ8 ✅ 𝗙𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲? -> Connect with me, re-share with your network & join my newsletter