Ron Ludwig

AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent https://lnkd.in/ggPnNpad Claude Opus alone: 31.71%. A weaker combo: 74.27%. This paper just validated everything MoJoAssistant was built on. Full breakdown → https://lnkd.in/gMme5v_Y

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At some scale, design systems stop being a style-guide problem and become an execution problem. Shared building blocks only help if teams can actually ship with them. The Intuit case study points at a big-team workflow problem: shared systems only help when teams can actually build and launch with them quickly. Read more: https://lnkd.in/dKhgKNUj

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Low-Level Design translates architecture into implementation using patterns, structure, and conventions. As generative AI accelerates code generation, the real value lies in knowing when human design insight is needed to complement and refine AI-generated outputs.

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Skilz: The Universal Package Manager for AI Skills The AI coding assistant ecosystem is booming, with tools like Claude, Gemini, Copilot, Cursor, OpenCode, Aider, Windsurf, Forge, and Zed AI all vying for developers’ attention. Yet a fundamental problem persists: no standardized method exists for installing and managing the specialized agents and agent skills. Each platform presents its own unique workflow, ranging from manual file copying to proprietary marketplaces or agent-specific plugin commands. This fragmented experience makes it difficult to replicate setups across machines, teams, and tools. Skilz solves this problem with a simple but powerful idea: Treat AI skills like software packages. Think pip install or npm install, but for AI agents. And if your tool doesn’t natively support skills, give it the prompts to do so.

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Vamana: The Graph Engine that eliminates the "Curse of Dimensionality." 🚀 When scaling AI systems (RAG, personalization) to billions of vectors, millisecond-scale retrieval is non-negotiable. Traditional indexing fails. My new article dives deep into Vamana, the state-of-the-art Approximate Nearest Neighbor (ANN) algorithm. It achieves unprecedented speed by fundamentally modeling the high-dimensional space as a navigable proximity graph. Key concepts inside: The magic of Robust Pruning (the $\alpha$ parameter). Architecting for speed and durability with Apache Cassandra. Read how this clever graph design makes predictable, reliable billion- scale search a reality: Rithish Saralaya, dr. Manoj Kumar https://lnkd.in/gU2pjDnU #VectorSearch #ANN #GraphTraversal #AIML #HighPerformanceComputing

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By allowing Alloy to run standard OpenTelemetry Collector YAML, the project has made a significant move toward the OTel community. I asked Torkel Ödegaard if the goal is to make the "Grafana Labs way" of doing OTel the industry default, and how much of this change was a direct response to users finding manual OTel configuration too difficult. Here is what he said about the future of Alloy and making OpenTelemetry easier to use.

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I'm bullish on spec-kit(https://lnkd.in/gu5pzsDk) & Spec Driven Development(https://lnkd.in/gy3i8-sg). It solves the problem of complicated prompts and keeping track of them—helping developers manage AI agent instructions through structured specifications rather than sprawling prompt files. Agent-OS(https://lnkd.in/ghGd5Twp) has a head start, but GitHub's resources and ecosystem make spec-kit hard to bet against. Either way, I think SDD is the near-term future of agentic development. Anyone else experimenting with spec-driven approaches in their workflow?

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Agentic AI role is exponentially increasing in software development lifecycle. Janus is an agentic tool developed by Vayavya Labs that automates part of this lifecycle for code compliance. For more information meet us at nasscom ai

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🚀 Unlock AI-ready Content with Adobe’s Optimize at Edge 🚀 If you’re thinking about AI discoverability and how your content performs with generative models, this is a game-changer. Adobe’s Optimize at Edge capability – part of Adobe LLM Optimizer – lets you apply AI-friendly enhancements at the CDN edge without touching your CMS or publishing workflows. 🔍 What it does: ✅ Serves optimized HTML only to LLM user agents, with zero impact on human visitors or SEO crawlers ✅ Delivers improvements in minutes — not weeks — with no engineering cycles ✅ Works with any CDN or frontend architecture (CMS-agnostic!) ✅ One-click rollback for speed and safety 👉 All this makes it a powerful no-code way to improve how AI systems see and interpret your content. Experience League 📈 How it helps your content: Optimize at Edge supports opportunities such as: • Recovering hidden content for AI visibility • Improving headings and structure for readability • Adding AI-friendly summaries and FAQs • Simplifying complex content — all injected at the edge for LLM traffic without altering your live UX. If you’re focused on AI optimization and content strategy in 2026, this deserves your attention. Read more on: https://lnkd.in/gCV3xfEp #Adobe #LLM #AIO #GEO #ContentStrategy #GenerativeAI

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In today’s world, being a strong generalist is a huge advantage. No matter where you work, adopt a builder mindset. Think like the founder of your space. Own the problem, wear multiple hats, and focus on delivering value to the end user. AI is accelerating everything. Those who can connect across domains and apply AI effectively will create the most impact. Being a generalist with a builder mindset is not just useful, it’s essential.

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Stay tuned for vikramvetal.ai! 🚀 Last week, I dived into the world of LLMs, Streamlit, and FastAPI to create an interactive chat-based riddle game inspired by the classic tales of Vikram and Vetal. Think Gandalf.lakera.ai, but with a rich narrative twist! For members who have not heard the story of Vikram and Vetal, they can refer to this link: https://lnkd.in/g7Wqgs3g" The structure of Vikram and Vetal is perfect for this: A story unfolds, the cunning Vetal poses a riddle, and the player (as Vikram) must answer correctly... or face the consequences (Vikram dies! 😱 No pressure!). But here's the cool part: In this modern adaptation, Vikram can *chat* with Vetal! Brainstorm, ask for hints, discuss the riddle – the possibilities are endless. The stories and visuals are all configuration-driven, making it easy to create new adventures. Could this be the future of engaging children's educational media? Fun quizzes for all ages? I'm planning to launch this as an AI experience at vikramvetal.ai. Sneak peek of the code is available here, so you can set up the game locally: https://lnkd.in/gHb2xCGJ Attached a video of the initial gameplay! What are your thoughts on using LLMs to create interactive and engaging stories? Share your ideas in the comments! 👇 #LLM #AI #Streamlit #FastAPI #GameDevelopment #EducationalGames #VikramVetal #GenerativeAI #MachineLearning Sharing for broader reach: Swaroop S Viswanath S Sameet Shetty Ramkumar Krishnan MBA, B.Eng John Maschke divya kodukula Arijit Roy Kanika Seth Aravind Kumar Suhas Ramesh Amrit Narayan Nayak Utsav Harsh Esha Dhing Nitya Keshari Jahnavi . Salla Tony Preeth Vishwank Vikram Singh Rahul Agarwal Ankit Gupta Abhishek Sagar Sudeep Kumar Datta Monojeet Nayak, CISSP Venu A.

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📚 Every tutorial makes RAG look easy. Then you ship it. Demo day: "What's our vacation policy?" → Perfect answer. Production day: "What's the policy for carrying over unused vacation days to the next year?" → Confident answer citing the 2019 policy. Not the 2024 update that replaced it. What changed? Nothing technical. Production queries are just harder. Ambiguity. Temporal reasoning. Multi-hop questions. Users who don't phrase things the way your documents are written. The tutorial assumed perfect conditions. Production doesn't care about your assumptions. Part 2A: Production RAG — What Tutorials Don't Teach You This is where the main series begins. Foundations done. LLM mechanics covered. Now we build. What's inside: RAG Architecture Evolution — Why naive "embed → search → generate" breaks, and what Advanced, Modular, and Agentic RAG patterns actually solve. Chunking Strategies — Why splitting at 500 characters is a recipe for incoherent embeddings. Recursive, semantic, hierarchical, late chunking, contextual retrieval. When each one wins. Retrieval Engineering — Dense vs sparse vs hybrid search. Why BM25 still matters in 2025. Reranking with cross-encoders. Query transformation patterns like HyDE. Framework Comparison — Haystack 2.x vs LangChain. Same concepts, different philosophies. When to use which. Vector Databases — Qdrant vs pgvector. Multi-tenancy patterns. The filtering problem nobody warns you about. Enterprise Concerns — Semantic caching, access control before retrieval (not after), cost optimization, LiteLLM integration for provider flexibility. It's a 90-minute+ read. It's dense. It's the stuff I wish someone had compiled before I started building RAG systems. Next: Part 2B, where we turn these concepts into a working Enterprise Document Intelligence system. Note: You can now read, and more than that, listen to the story on https://lnkd.in/gvf_JwEK as well. 😉 . #AI #RAG #SoftwareArchitecture #Engineering #LearningInPublic #Haystack

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Chunking and snippet extraction has been a huge focus lately - my latest blog dives into some of the work we've done on this to date, including support for a chunk rescorer in our semantic reranking retriever, as well as some useful ES|QL primitives to get more visibility into chunks and snippets. #elasticsearch #semanticreranking #snippets #chunks #chunking #esql https://lnkd.in/e6UD7iii

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Principle #6: Insights Must Drive Action From the 10 Principles for Building a Resilient and High-Impact APM Program Knowledge has never been more available. From stories once passed orally across generations, to the printing press, to the internet, and now to large language models, humanity continues its quest to make knowledge instant and abundant. We’ve lived through the Information Age and now stand in a time where knowledge is increasingly commoditized. What once took decades to master can now be accessed in moments. Knowledge alone is no longer the differentiator. We are entering the Wisdom Age, a time where the true advantage lies in the ability to convert information into action. Wisdom, unlike knowledge, isn’t free yet. It must be earned. It requires experience, context, accountability, and a willingness to learn. As the saying goes: you must “pay” attention. In Asset Performance Management, the same truth applies. Insights, whether from condition monitoring, corrosion models, predictive analytics, or SIS verifications, must not remain passive. Awareness alone does not prevent failure. APM programs must be architected to translate signals into timely and accountable smart decisions. One of the clearest examples of wisdom in APM is the trade-off between planned and unplanned downtime. With the right insights, organizations can shift from reactive interventions to proactive actions, accepting a short, planned outage today, to avoid a prolonged, unplanned crisis tomorrow. This is an operational decision directly impacting the business, protecting throughput, cost structure, and safety. Work orders must be issued. Shutdowns must be justified. Investigations must be triggered. Safety reviews must be escalated. Each insight must lead to a structured response, documented, traceable, and continuously improved. When insights don’t drive action, organizations remain stuck in observation mode. They predict failure just to fail preventing it. When wisdom is embedded into the system, when APM is designed to detect and to respond, insights become results. Organizations become proactive, disciplined, and trusted stewards of asset performance. In short: Knowledge tells you what could happen. Wisdom ensures you do something about it, before it costs you more. #APM #AssetPerformanceManagement #IndustrialExcellence #Reliability #DigitalTransformation #MaintenanceStrategy #OperationalDiscipline

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❤️ Loved this from Harshit Omar Cloud outages aren’t anomalies; they’re warnings. Most enterprises are architected as if hyperscalers are “too big to fail,” but recent outages have proven the opposite. Resilience needs a new baseline: Can you run somewhere else when your provider falters? That’s the promise of #CloudCloning and true #multicloud optionality. Big thanks to MR Rangaswami Sir for the conversation. Worth the read.

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A recent white paper from #anthropic demonstrated that when one LLM is distilled from another with the same base architecture and initialization, the distilled model is subject to “subliminal learning.” (https://lnkd.in/gqRkxQ7S) In other words, the student model can pick up hidden traits or biases from the teacher model even when those biases are not explicitly present in the training data. What was really interesting is this result showed up when the teacher model generated random sequences of numbers, or even working Python code, that the distilled model trained from. Why this matters beyond theory: > Hidden bias transfer: Traits from the teacher (or even subtle biases in the system prompt) can show up in the student model even when overt bias is filtered out of training data. > Synthetic data feedback loops: As the internet fills with AI-generated text, code, and images, future models trained on this data may inherit the hidden biases of the source models. > AI detection: This paper suggests it may be possible to identify the source model of a distilled model or even detect whether AI was involved in a piece of writing or code. > Architectural diversity: To mitigate subliminal bias transfer, it may help to incorporate models with different architectures in your multi-turn, agentic workflows. For example, use gpt-5 for the heavy lifting and refine the work with Qwen3-235b-a22b (a model with a completely different architecture). In theory, the Qwen3 model can help negate some of the bias of the original model.

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If you were to cruise along California’s famous Pacific Coast Highway, heading west from Santa Barbara towards Los Angeles, you might pass by a small town called Summerland. This unassuming little settlement might not catch the eye for anything except its evocative name - and a rather handsome-looking stretch of beach. But Summerland is, in fact, considered by many to be the first seaside oil drilling enterprise in the world. Offshore drilling has since gone global - but as the waters get deeper and the technology becomes more sophisticated, will the offshore industry be able to handle today’s technical, social, and environmental challenges? Full story 🔗 https://okt.to/41EChl Subscribe to our free biweekly newsletter 📥 https://okt.to/f4M3eD Follow Our Industrial Life to learn more.

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✨ Wrapping up IBC & RDK Summit – Insights, Lessons, and What’s Next The past week at IBC and the RDK Summit have been nothing short of transformative — from big ideas to actionable strategies. 🔑 Key Takeaways At IBC, conversations revolved around AI-driven workflows, monetization models, and creating differentiated user journeys. At the RDK Summit, the emphasis was on making RDK operator-friendly — ensuring faster deployments, simplified integrations, and consistency across devices. 📚 Lessons Learned Storytelling and use cases matter more than just technology — whether it’s sports enhancements that redefine live experiences, or Kids OTT platforms built for safe, engaging entertainment. Operators are looking for practical innovation — solutions that are easy to adopt, deliver ROI, and scale with user expectations. The future of media operations lies in AI-powered management, ensuring agility, automation, and efficiency at every step. 🚀 Hero Solutions We’re Driving Forward - Sports Enhancements – smarter fan experiences powered by real-time insights. - Kids OTT – safe, fun, and personalized platforms for the next generation of viewers. - AI-based Managed Media Operations – intelligent automation that reduces complexity and accelerates innovation. ⏭️ Next Steps - Strengthen collaboration with operators and partners to drive operator-friendly RDK deployments. - Keep building narratives around real-world use cases rather than just technical features. - Translate the momentum from IBC - International Broadcasting Convention & RDK Summit into tangible solutions for the industry. A big thank you to everyone who engaged with us — your ideas and energy are helping shape the next chapter of media and connected TV. #IBC2025 #RDKSummit #SportsTech #KidsOTT #AI #ManagedMedia #InnovationsinMedia #LTTS Atul Kapur Jitesh Kumar Popat Anshuman Singh Ruben Thalakkottur Joseph Ajith Prasannan [Ajith] Andy Gangotra Rajarajan A Lakshmikanthan (RR) Paddy Viswanadhula Ruuchi Padhi Ashish Moghe Bhavna Oberooi

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