Production-Grade GenAI: Beyond the Proof of Concept. Scaling LLM systems isn’t just about prompt tuning or spinning up a demo. It’s about architecting for uptime, cost control, and seamless integration with your real-world data and workflows. Our team lives at the intersection of backend engineering and AI R&D—shipping secure, scalable GenAI that delivers measurable business value, not technical debt. Curious how enterprises are moving from pilot to production—without compromising reliability or speed? Let’s connect for a technical deep dive. 🚀

𝐌𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐭𝐞𝐦𝐩𝐭𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐡𝐚𝐫𝐝𝐞𝐫 𝐭𝐨 𝐤𝐧𝐨𝐰 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐧𝐠𝐬 𝐛𝐫𝐞𝐚𝐤. That’s one of the “seven deadly sins” of LLM development John Berryman (Arcturus Lab, ex-Githhub, coauthor of O'Reilly's Prompt Engineering for LLMs) has seen in practice. So how do you keep things simple enough to debug... without losing power? That’s the focus of my conversation with John on Vanishing Gradients. We walk through the “seven deadly sins” of LLM development, the fixes, and the mental models that actually help AI teams ship: 𝐜𝐨𝐧𝐜𝐫𝐞𝐭𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐧𝐝 𝐚𝐧𝐭𝐢-𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐟𝐨𝐫 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈. Listen, watch, or read: Apple → https://lnkd.in/g3_c2BQx Spotify → https://lnkd.in/gkeTXxSZ YouTube → https://lnkd.in/gGGsJfsa Full notes → https://lnkd.in/gevcirsD This clip is John on why simple pipelines often outperform multi-agent orchestration:

Really great discussion between Hugo Bowne-Anderson and John Berryman about getting actual use out of LLM based systems (agentic and otherwise). Some confirmation-bias-induced takeaways: * Start simple, add complexity only as needed. * Systems will not be 100% accurate and should NOT be used without human intervention where 100% accuracy is required. * Humans must be in the loop and able to evaluate source data. * Information granularity -- just because you can stuff a million tokens into a context window doesn't mean you should. I'm looking at you 1,000+ page PDFs! 😂

“Most companies eventually find they have trapped value — experiments that almost became full products, but got left on the shelf,” said Simon Corry, Senior Director of Product Design at Ramp when he sat down with Double Diamond last month. At Ramp Labs, those experiments get a second life. By building AI projects that can ship in hours, not months, the team turns ideas into real products — while engaging a growing community of builders who want to work on exactly these kinds of problems. We also talked about velocity, user-facing intelligence, and the rise of the “super IC.” Watch or listen to the full conversation on Substack:  https://lnkd.in/eqTb3wNW

🤔 People who think AI can replace developers and build entire apps on its own probably haven’t spent enough time actually using it to code, build, or launch something real.

Stop reading research papers. Start seeing how AI actually works in the wild. Forget F1 scores. Forget theory. Let’s see how the giants handle real users, real traffic, real stakes: Here are 9 engineering blogs showing how AI ships at scale: 1️⃣ AWS GenAI Blog – From 0 to 100K in seconds: Instant Scale with AWS Lambda 100K requests/second. No meltdown. 2️⃣ Netflix Tech Blog – Supporting Diverse ML Systems at Netflix 260M users. Every click predicted. Zero downtime. 3️⃣ Airbnb Engineering – Why Boobo? $75B in bookings. All ranked by ML in real-time. 4️⃣ LinkedIn Engineering – Feed Relevance 1B members. Personalized feed delivered instantly. 5️⃣ Uber Engineering – D3: An Automated System to Detect Data Drifts Feature drift detection that saved millions. 6️⃣ Spotify Engineering – Humans + Machines: A Look Behind the Playlists Powered by Spotify 600M playlists. Each one unique. How? 7️⃣ LangChain Blog – LangChain's Second Birthday From prototype → production LLM apps. 8️⃣ Hugging Face Blog – Model Deployment Considerations Deploy models without the 3am panic. 9️⃣ Meta AI Blog – PyTorch to Production at 3B Users Daily PyTorch → production at 3B users daily. 🔥 The pattern? They don’t care about your metrics in a lab. They care if it survives when 10M users hit “refresh.” 💬 Question for you: Which production failure taught you the most? #MachineLearning #AIatScale #Engineering #TechBlogs #BigData #MLinProduction #SoftwareEngineering

I urge marketers to take a look at what's going on in the tech sceneright now. There are some master class lessons on world building, product development, content creation and being part of the cultural conversation. All very important concepts that I see no one talking about but will help you navigate this AI era. Dropping my thoughts on it this week.

How Netflix transformed real-time user experience with smarter data strategies. By moving from static queries to GraphQL mutations and adding cache normalization, Netflix achieved what most teams strive for: ⚡ Faster responses 💡 Lighter payloads 🌍 Real-time personalization At Lex AI Labs, we love exploring how top tech teams use AI-driven logic and system design to create scalable, seamless experiences. Because great design is not about doing more, it’s about doing smarter. #LexAILabs #AI #Innovation #TechDesign #Scalability #DataEngineering #FutureOfWork #MachineLearning

"Every new AI tool seems like another monthly bill, making it feel less like progress and more like déjà vu from the streaming wars. When you tally up all those subscriptions—for writing help, image creation, productivity boosts—the costs and choices quietly pile up for households, students, and businesses alike. There’s real potential here: AI can personalize learning, speed up boring tasks, and spark creativity in ways we haven’t seen before. But we risk locking powerful tools behind paywalls, creating new digital divides, and overwhelming ourselves with too many choices that may only deepen the sense of “subscription fatigue.” Like the streaming boom, what began as empowering could end in exhaustion. Pause before signing up impulsively; ask yourself which tools truly solve a problem for you, not just add to your tech clutter. In the end, is more always better, or are we mistaking abundance for value?"

𝐓𝐡𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐥𝐢𝐟𝐟: 𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐬 𝐖𝐡𝐞𝐧 𝐀𝐈 𝐓𝐨𝐨𝐥𝐬 𝐅𝐚𝐢𝐥 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧? In our new episode of Vanishing Gradients, Alex S. (ML Engineer, ZenML) and I explore this question through the lens of 900+ documented LLM and AI enterprise deployments. We dive into insights from the LLMOps database: a rich resource Alex and his team built to document real-world AI deployments. This conversation originated as a Q&A from our Building with LLMs course, which kicks off again on Nov 3: https://lnkd.in/gqyhmWr2 Listen to the full conversation and consider contributing your own deployment to the LLMOps database. Apple: https://lnkd.in/gJpRii7r Spotify: https://lnkd.in/gcfGYNyv YouTube: https://lnkd.in/g_Z5QzcF Other episodes and show notes: https://lnkd.in/gMYyBkpa