Curious about what is really happening in the world of open source? We are thrilled to launch our brand new weekly video series: 'Open source: the inside track' Every week, we are handing the mic over to the brilliant minds on our team who live and breathe open source. We want to highlight the incredible technical expertise right here in our community and share it directly with you. What to expect: ➡️ Real, user-generated insights from our team ➡️ Deep dives into everyday open source challenges ➡️ A closer look at the people behind the code Watch our very first video below to hear from Product Manager Alex Bunday when he answers our question "Which open source tools or frameworks do you find most exciting now, and why?" Then visit our website to learn more about how we can help you navigate the tech landscape: https://bit.ly/3ShJ60w #OpenSource #OpenSearch #AI #TechCommunity #InsideTrack #NetAppInstaclustr
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Google's Gemma 4 just launched — open source, Apache 2.0 license, downloaded over 400 million times across its prior generation. Open-source AI is reshaping the competitive landscape. The models that win will be the ones developers can actually build on. For Muslim engineers building in AI: the open-source ecosystem is your leverage. Use it. → https://lnkd.in/e8N2QgDY #OpenSourceAI #Gemma4 #MuslimTech #AIEngineering #TechInnovation
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Over the past few days, I’ve been exploring Claude Code quite extensively. Initial impression: promising, with a lot of potential. There’s already a growing set of best practices around how repositories should be structured to get the best results, primarily centered on well-crafted documentation that guide the model’s behavior. But this raises an interesting concern. While these tools abstract interaction into natural language, we’re simultaneously creating a parallel layer of documentation that directly influences output quality. In a way, we’re shifting effort from writing code to curating context. And unlike code, this layer can become obsolete very quickly. So the question is: What does a sustainable approach look like here? Do we rely on automated workflows to continuously update and maintain this documentation? Can we trust these systems to preserve output quality over time? Or are we just introducing a new form of technical debt - less visible, but equally impactful? Curious to see how this evolves over the next 6–12 months. #AI #LLM #AICoding #DeveloperExperience #TechStrategy #EngineeringLeadership #FutureOfWork
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Google just flipped the script on open-source AI. 🚀 Gemma 4 is here, and it’s not just a small upgrade, it’s a total shift in the ecosystem. From a massive jump in coding capabilities to the new Apache 2.0 license, the barrier to building high-performance AI agents just vanished. I’ve broken down the 5 biggest changes you need to know in the slides below. Key Highlights: Full Freedom: Now under Apache 2.0. Expert Coding: Massive ELO jump (from 110 to 2,150). Native Multimodality: No more "bolted-on" vision or audio. Reasoning: 4,000-token internal "Thinking Mode." Check out the carousel for the full breakdown! 👇 #GoogleAI #Gemma4 #OpenSource #AI #WebDevelopment #TechTrends
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I recently attended the “Azure Decoded: Ground AI Apps with Fabric IQ’s Semantic Foundation” session, and it was a really insightful experience. I’ve been working with AI to map out databases, creating a roadmap and even a “story” of how processes flow within them. What stood out to me in this session is that this approach actually has a name: ontology. The session explored how Fabric IQ creates a shared semantic foundation, allowing both teams and AI to reason over data in a consistent and meaningful way. By unifying business entities, relationships, and metrics across data, models, and systems, it enables more accurate and trustworthy insights. What I found particularly valuable: Understanding how semantic foundations improve AI reasoning Seeing how data agents can ground natural language queries in real business context Learning how this approach reduces prompt complexity and improves reliability It was great to see how these concepts can be applied in real-world workflows, helping data engineers and AI developers move faster from data to decisions. Looking forward to exploring more around ontology-driven data platforms and how this can enhance the way we build and interact with data systems. #Azure #FabricIQ #DataGovernance #AI #Ontology #DataPlatform #MicrosoftReactor #DataEngineering
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📊 Data Science: More Than Just Numbers When people think of data science, they often focus on algorithms or dashboards. But in reality, it’s a powerful ecosystem that connects analysis, interaction, infrastructure, and impact. 🔍 Analysis From data mining to NLP and modeling this is where raw data becomes meaningful insights. 🤝 Interaction Search systems, recommendations, and conversational AI making data usable and accessible. ⚙️ Infrastructure Secure systems, scalable environments, and high-performance computing the backbone of everything. 🌍 Impact From trustworthy AI to evidence-based decisions this is where data science shapes society. 💡 The real value of data science isn’t just in building models it’s in creating responsible, scalable, and human-centered solutions. #DataScience #AI #MachineLearning #BigData #DigitalTransformation #Analytics #Innovation #TechForGood #DataDriven #FutureOfWork 🚀
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🚀 From Curiosity to Building Real AI Systems! When I first started exploring LLMs, I was amazed by what they could do… But deep down, I knew — this is just the beginning. I kept wondering: 👉 Can AI remember conversations? 👉 Can it use tools like a human? 👉 Can it actually make decisions? That curiosity led me to dive into LangChain — and honestly, it changed everything for me. I didn’t just learn how to use AI… I learned how to design AI systems. 💡 Through this journey, I explored: 🔹 How LLMs, Prompts, Chains, and Memory connect 🔹 How Agents can think and act using tools 🔹 How to build modular, real-world AI applications And the best part? I implemented everything with working code 🚀 📘 I’ve documented my entire learning in a deep technical blog: 🔗 Blog:https://lnkd.in/gdNeHPKq GitHub: https://lnkd.in/g7GiBYKf 🙏 A heartfelt thanks to Innomatics Research Labs for giving me the opportunity, guidance, and environment to explore Generative AI in such depth. This experience truly helped me move from: 👉 Learning AI → Building AI systems Excited to keep growing in this space and explore what’s next . #LangChain #GenerativeAI #AIEngineering #LLM #Python #MachineLearning #OpenAI #DeepLearning
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🚀 This Week in Code Assistant: Fastest-Growing Projects — April 21, 2026 This week in the Code Assistant space, we're seeing a surge in popularity of tools that simplify interactions with AI models and provide efficient ways to work with code. The trend is shifting towards... Read full report → https://lnkd.in/dak7hDvR #AI #OpenSource #GitHub #Tech #CodeAssistant
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Anthropic briefly banned the creator of OpenClaw from accessing Claude, adding more tension to the growing clash between closed AI platforms and open source tools. The incident came shortly after Anthropic changed how third-party tools like OpenClaw are billed, pushing users toward API-based pricing and raising fresh questions about platform control, competition, and developer trust. Read more: https://lnkd.in/drNBEBbZ #Anthropic #Claude #OpenSource #AI #Developers
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Everyone is mourning the death of open source AI this week... but they're looking at it backwards. Anthropic built Claude Mythos—93.9% on SWE-bench, finding zero-days hidden in operating systems for decades—then locked it inside a consortium of 11 companies. You can't have this one. Meta shipped Muse Spark from its new Superintelligence Labs. Closed. The company that made open-source AI its identity kept this one proprietary. The conventional read: the frontier is pulling away from the public. Bad for the ecosystem. The contrarian read: this might be the best thing to happen to professionals building AI workflows. The era of "pick the best model and build around it" was always fragile. Every team that went all-in on GPT-4o had to migrate when OpenAI retired it. Every workflow tied to one provider's API is one policy change away from breaking. Restricted access forces model-agnosticism. Model-agnosticism forces investment in the one thing that's actually portable: context. Reasoning patterns, domain expertise, professional knowledge, structured so any capable model can use it. That transfers across providers. It survives access changes. It compounds regardless of which lab is ahead this quarter. The model is the thing they can take away. Context is the thing you keep. #ContextEngineering #ProfessionalAI #AIAgents #FrontierAI
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🚀 This Week in Image & Video Generation: Fastest-Growing Projects — April 11, 2026 This week in Image & Video Generation, we're seeing a surge in open-source tools that empower creators to produce high-quality ... Read full report → https://pullrepo.com #AI #OpenSource #GitHub #ImageVideoGeneration
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