Devsu’s Post

🚀What if you could “work in public”… without being open-source? Teams building closed products usually ship in silence. But the AI era doesn’t reward secrecy — it rewards speed, transparency, and real-world usage. So instead of hiding behind NDA walls, some teams are doing the bold thing: 🔹 Shipping a beta early 🔹 Letting users break it 🔹 Iterating in real time 🔹 Going live with new models within hours (sometimes minutes) Working in public isn’t just a dev culture shift — it’s a competitive advantage. Real usage beats perfect planning every time. 🎤Speaker: Aparna Sinha, SVP of Product at Vercel 🔗Watch her full talk on “The Velocity Advantage: How AI-Native Teams Ship Products Fast and Safe” at https://lnkd.in/gPgcnkQv   #ProductCon #ProductManagement #AIProductManagement

If your product team is drowning in meetings, handoffs, and alignment decisions (the classic "Large Silo" problem), your structure is the enemy of speed. The answer is Co-Creation. We must empower small, cross-functional units by integrating AI directly into the decision-making loop. It's a collaborative force where Business and Tech Co-Define value and quality, using the AI Partner to automate the execution, testing, and deployment. This model cuts through bureaucracy to deliver Real-Time Decisions and Team Autonomy. Stop managing the overhead, and start shipping. #AgileTransformation #ProductStrategy #AIAdoption #BusinessTechAlignment

Opening story: I once watched a product sprint accelerate when an AI agent handled data gathering and initial triage, freeing the team to focus on insights. Latest coverage on AI, AI Agents, SaaS, and Tech from top outlets shows agents becoming practical copilots, linking apps, steering multi-step tasks, and surfacing context for better decisions. The pace is rapid, with emphasis on safety, governance, and explainability as agents scale across business workflows. SaaS platforms are embedding copilot capabilities to streamline processes, while no-code tools enable more teams to design AI-powered automations. Costs and ROI are central talks as models improve and infrastructures mature. Personal thought: these agents are not here to replace people but to extend judgment and curiosity. What part of AI agents: speed, accuracy, or governance, are you most excited to double down on in your team?

What does it really mean to build AI-native products, and why does it matter for your business? An AI-first approach to product development treats intelligence as infrastructure, not an enhancement. This transforms how teams innovate with real-time insights and iterate on products with continuous learning, driving predictable product releases deeply aligned with evolving customer expectations. For enterprises, this translates to faster time-to-value, reduced operational friction, and greater market competitiveness.    In his latest blog, Sriram Natarajan, our SVP of Digital Product Engineering, dives into how product teams must reimagine their development lifecycles through the lens of data and AI to drive greater agility, scalability, and continuous innovation.   Read the full blog: https://lnkd.in/gDgtP_Xj   #AInative #Digitalproductengineering #AIinnovation #AIinSDLC #Marlabs

Morning coffee, a prototype buzzing in the background, and a realization: AI is becoming the backbone, not just hype. Across AI, AI agents, SaaS, and tech, practical adoption is accelerating as teams embed LLMs into workflows, build intelligent agents to automate repetitive tasks. Governance and transparency are rising as essentials. Analysts highlight modular architectures and security-first design. Startups and incumbents share lessons on weaving AI into customer experiences, product pipelines, and backend ops, not just pilots. The takeaway: small wins come quickly when teams pilot iteratively and measure impact. To me, AI should augment human judgment, not replace it. How are you deploying AI agents to drive value in your team this quarter?

🚀 Wrapped up The Complete GenAI Launchpad by Product Space , a 4-day deep dive into building real, working GenAI 🚀 Wrapped up The Complete GenAI Launchpad. A 4-day deep dive into building real, working GenAI systems. Here’s what we covered: 🧩 Agentic AI – Building, testing & deploying intelligent agents 📚 RAG Systems – Connecting LLMs to real-world data 🪶 Prompt Engineering – Designing prompts that think, not just respond ⚙️ MCP Framework – Building modular, scalable AI systems 💡 My biggest takeaway: GenAI isn’t just about prompting, it’s about designing systems that reason, retrieve, and act with context. Grateful for the incredible mentors and peers who made the sessions so hands-on and inspiring. Excited to apply these learnings in product and AI workflows ahead! #GenAI #AIEngineering #ProductManagement #LearningJourney #RAG #PromptEngineering

🔍 3 reasons Gen AI fails in enterprise: 1. Great tools. No integration. 2. Fast dev cycles. Slow teams. 3. Everyone’s waiting for "the next model." Stop waiting. Start orchestrating. 👇 Link in the comments #AIIntegration #LLMStrategy #AIExecution #HatchWorksAI #AIAdoption


📈 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗻𝗼𝘃𝗲𝗹𝘁𝘆 𝘁𝗼 𝗻𝗲𝗰𝗲𝘀𝘀𝗶𝘁𝘆. What began as isolated pilots in 2024 is now reshaping how portfolio companies work, create, and deliver value. The next advantage comes from scaling GenAI — embedding it across functions where creativity meets execution. 💬 𝗪𝗵𝗶𝗰𝗵 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝘀 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲? #GenerativeAI #WorkflowAutomation #DigitalAdoption #PortfolioGrowth #ClaymorePartners

We’ve reached a strange point: MVPs are no longer minimum viable. In AI especially, teams tend to overbuild their first iteration (multi-agent pipelines, dashboards, retraining cycles, ...) all before validating a single decision loop. They ship complexity before they ship learning. But true MVPs aren’t dumb. They’re built to be proven wrong, fast. The smartest teams don’t chase success: - they engineer feedback - they design for uncertainty rather than scale - they make failure cheap and visible - and they build systems that learn before they optimize. Because an MVP that doesn’t learn isn’t a product: it’s a demo. And that’s where most AI teams get stuck: they validate architecture, not behavior. They optimize infrastructure before understanding how their system actually learns. Few build feedback factories, systems that improve precisely because they’re used. Real product maturity isn’t about building more. It’s about building less, with more intention, and faster learning loops. #RightComplexity #AIWithoutMyths #EngineeringReality #ProductMindset

The #GenAI tools you use in your daily are as important as the whole #GenAI ecosystem you intentionally leverage. How do you design your tool stack in a way that it is integrated, intentional and optimized around your specific needs? A lot of necessary integration will take place in the next 2-5 years. For now, many of us have to spend time and map the tools, use cases and possible integration that can give us most value back in terms of time, ideation, quality of deliverables and more.