Redwerk

Your AI is growing fast. But bigger ≠ better. We recently published a deep dive on scaling AI models without losing quality, reliability, or budget. It's full of real examples from Meta, Uber, LinkedIn, and Netflix. Here's what we learned: 🔹 Match size to data. A smaller model trained on vast amounts of data beats a larger model on small datasets. Train longer on more data, rather than just adding parameters. 🔹 Architect early. Plan for model parallelism, pipeline parallelism, tensor operations, and sharding before you scale. One GPU works fine now—multi-GPU won't without the right foundation. 🔹 Use sparsity & retrieval. Mixture-of-Experts (MoE) activates only a fraction of weights per token, cutting compute without sacrificing accuracy. RAG lets you tap external knowledge without retraining. 🔹 Optimize inference. KV-cache reuse and speculative decoding speed up token generation. Shadow mode → canary release → A/B testing catches problems before they hit users. 🔹 Choose your scaling direction. Scaling up costs resources but boosts performance. Scaling down optimizes one model. Scaling out breaks it into specialized models—more complex, but more flexible. 🔹 Monitor relentlessly. Continuous drift monitoring with real-time evaluations on live data slices keeps quality high and catches failures early. Meta's Llama 3 required 15 trillion tokens and heavy filtering. Uber's Michelangelo brought unity to ML chaos on one platform, reducing costs. LinkedIn's Pro-ML was about unifying the infrastructure. Netflix built multi-layered systems to handle humongous data. 🤔 The pattern? Scaling isn't about making systems bigger; it's about strategy, architecture, and relentless monitoring. Ready to scale smarter? Read the full breakdown: https://lnkd.in/d7HaXNPc

🇪🇺 Europe's calling. Here's where we're going in 2026. We mapped the top tech events across the continent, from Spain to the UK and beyond. The lineup includes innovation conferences, developer summits, Web3 showcases, and startup stages. Don't miss out. Attend the ones that matter to your area. Did you find your next must-attend event? Share it in the comments 👇 See the full curated list (50+ events worldwide): https://lnkd.in/dkf3aEqz

We reviewed 18 North American tech events happening in 2026. Only a few are worth the trip. ✈️ AI is a major focus at events like ProductWorld, AI DevWorld, WWDC, and AI Con USA, which cover everything from LLM engineering to product strategy. GDC still stands out for gaming and immersive teams. Web Summit Vancouver and TechCrunch Disrupt are also important if you're looking to raise money or validate early-stage ideas. Ticket prices range from $149 for CES to $3,000 for FUELD. The return on investment varies widely. 💰 Our approach is to focus on depth rather than breadth. Choose two events. Valuable conversations are better than crowded stages. 📋 For a complete breakdown, visit: https://lnkd.in/dkf3aEqz

🤖 71% of organizations already use generative AI. But the real challenge is scaling it without hurting performance. We just published a detailed look at AI model scaling, focusing on strategies that maintain quality as you grow. Inside: 👉 Why "bigger" does not mean "better" (Chinchilla by Google DeepMind outperformed GPT-3 by OpenAI with less computing power) 👉 Scaling up, down, and out; which path suits your system 👉 Sparsity, RAG, and speculative decoding: tools that reduce latency and cost 👉 Real cases: Meta's Llama 3, Uber's Michelangelo, Netflix's media ML stack 📰 Read the full breakdown: https://lnkd.in/d7HaXNPc

AI can come up with ideas, create wireframes, and process specifications in minutes. That's true. We've delivered over 50 MVPs this way. However, we learned that founders who fail aren't just moving too fast; they’re validating the wrong things. We observed a founder launch an MVP in 14 days using only AI. By week three, everything fell apart. He rushed through without confirming whether anyone actually wanted his product. This is the gap AI cannot bridge. It’s not because AI lacks intelligence, but because validation needs human judgment to determine which metric truly shows product-market fit for your business. 🧭 Here’s what we actually do: ⚡ AI generates ideas quickly. We validate the right thing together. Before you start coding, identify your primary metric — not “daily active users” or “feature adoption.” What truly shows that customers want this enough to change behavior or pay? Validate that before scaling. 🛠️ AI can write specifications and code. We review for architecture, not syntax. About 82% of developers use AI for coding. What sets successful MVPs apart is that someone with product experience reviews the blueprint and asks: ⚪ Does this architecture support our revenue model at 10x size? ⚪ What security assumptions could cause problems? ⚪ What is missing that should be included? AI overlooks these because they’re business decisions, not technical ones. 📊 AI processes feedback. We interpret it. Feed user feedback into an LLM, and you might see: “Users mention slow load times in 23% of messages.” Talk to users, and you hear: “Slow load times make me distrust my data security.” This difference between a feature problem and a trust issue alters everything about what you decide to build next 📈 The real difference: Over 19 years and 50+ MVPs, we’ve seen that founders who succeed after launch aren’t necessarily smarter or faster — they’re clearer about what they’re proving. Those who fail treat validation as optional. They deploy whatever AI generates. They follow what seems good or technically feasible. Those who succeed? They answer one question: “Do paying customers want this?” AI speeds up many processes, but not critical decisions. That’s where you stay in control. 💬 What metric are you validating before you ship your MVP? Most founders we speak with can’t answer that question clearly —and that’s often why they struggle. We developed a framework for this problem, along with three architectural review questions that capture what AI overlooks. We also explain how we distinguish “feature problems” from “trust problems” in user feedback. 📖 Read the full article: “Supercharging MVP Development with AI: Low-Code, No-Code, and Automated Specifications”: https://lnkd.in/deBdGBVK

Stepping in after a vendor failure requires more than fresh code—it demands a structured reset. 🔧 When Pridefit's previous development partner stalled, we started with what matters most: a full technical audit. What we found: 🚫 Technical debt is blocking new feature velocity 🚫 Missing analytics infrastructure 🚫 Incomplete feature backlog from the previous vendor What we delivered: ✅ Complete code and infrastructure review ✅ 4 analytics integrations (AppsFlyer, Mixpanel, GA4, Facebook Pixel) ✅ 6 new features shipped — multi-coach support, Bootcamps flow, improved onboarding ✅ Clean infrastructure migration to client ownership 📊 Result: 45% increase in subscriptions. The distinction: Technical stability and business growth aren't parallel—one enables the other. When velocity stalls, the diagnosis always precedes the fix. For teams facing similar constraints: start with visibility. Understand what's broken before committing to a timeline. 👉 Case study: https://lnkd.in/dGytTca2

🎯 Proud moment: our QC engineer just explained why testers miss obvious bugs. It’s all about psychology. 🧠 This past Saturday at Quality Assurance Day UA 2025, Dmytro Penia, QC Engineer at Redwerk, took the stage with a talk that truly stood out: “Cognitive QA – How the tester’s brain works and why we miss obvious bugs.” 💡 The main idea: A dropdown in a form went unnoticed in production for three sprints. This wasn’t a team failure — it was a cognitive blind spot. Here’s what Dmytro discussed: 🔹 Confirmation bias: We look for proof that things work, not proof that they fail. 🔹 Inattentional blindness: When you focus on one thing, you overlook everything else. (Remember the invisible gorilla experiment?) 🔹 Anchoring, recency, sunk cost: Less obvious, but equally harmful. These aren’t flaws in testers — they’re characteristics of how our brains function. Once you understand them, you can work around them. 🧩 Dmytro’s tools that really help: ✅ Pair testing – two brains see more than one. ✅ Anti-checklists – encourage unconventional thinking. ✅ Exploratory charters – promote systematic creativity. ✅ Bias checklist cards – help catch yourself mid-bias. 🚀 Key takeaway: QA isn’t just about technical skill. It’s about understanding how you think, so you can start thinking in new ways. Congratulations on the speech! We're proud to have Dmytro on the team. 👏

From Broadway actor to building a fitness app 💪 Sam Leicht's story on leading tech development without a tech background. Sam Leicht, founder of Pridefit, interviewed 30 development companies before choosing us. Here’s why. 💬 In this Tech Talk, Sam shares: → Managing app development as a non-technical founder → Why sustainable health is Pridefit’s main principle → Which new features attracted the most members → How communication with your development partner makes a difference 💡 What stood out: “The baseline with any development partner is the ability to write the code. What sets Redwerk apart is our ability to communicate effectively.” 🚀 You don’t need a computer science degree to build a real product — just the right partner. 🔗 Read the full Tech Talk → https://lnkd.in/dgqAexqx

Banning AI doesn't stop employees from using it—it drives them underground. 60% of employees paste company data into genAI tools, even when forbidden. They create personal accounts with zero security. The result? Your sensitive data is scattered across untraceable free accounts. Our CEO explains why AI bans backfire and outlines a 4-step framework for responsible adoption. Read the full article 👇 https://lnkd.in/dpRRF5-j