novatechflow | Alexander Alten

PDFs are everywhere, but traditional search tools barely go beyond glorified Ctrl+F. This article explores how Large Language Models and Retrieval Augmented Generation can turn static PDF archives into an intelligent, contextual knowledge base that answers real questions instead of just returning files. It walks through a DIY setup built with langchain, transformers and FAISS that loads PDFs, chunks their content, embeds them into a vector store and then uses an LLM to answer questions grounded in the original documents. The result is a practical, self-hostable way to search and reason over your existing PDFs with far more nuance, less hallucination and a clear focus on useful, organisation-specific answers instead of abstract AI hype. PDFs are everywhere, seemingly indestructible, and present in our daily lives at all thinkable and unthinkable positions. We've all got mountains of them, and even companies shouting about "digital transformation" haven't managed to ...

Running Llama 3 locally on your MacBook gives you privacy, reliability and speed that the cloud often can’t. Instead of trusting flaky networks and remote servers with your ideas, you keep everything on your own machine, work offline without interruptions and get snappy responses from smaller, well-tuned models like Phi-3. With Ollama handling local model management and easy integration of Hugging Face GGUF models, you can spin up Llama 3 or custom models, tweak templates and experiment freely without cloud limits or surprise bills. For product ideation, this setup means you can brainstorm, prototype and refine deeply specific prompts and workflows in your own sandbox, on your own hardware, with full control over data and experience. I'm gonna be real with you: the Cloud and SaaS / PaaS is great... until it isn't. When you're elbow-deep in doing something with the likes of ChatGPT or Gemini or whatever, the last thing you need is your AI assistant starts choking (...

This article argues that modern product work is not about headcount, rituals or corporate theater, but about small, empowered teams building things customers actually care about. It uses a failed “big team equals importance” sustainability project as a cautionary tale and then lays out core principles for healthy product organisations: cross-functional teams, end-to-end ownership, flexible methodology, a crystal-clear vision, real empowerment instead of micromanagement, tight customer feedback loops, outcome-focused metrics and protecting team health. Drawing on Nike’s classic “our business is change” mindset and Spotify’s experimentation culture, it frames everything in the company as a product – from internal processes to customer experiences – and calls for ditching corporate bullshit in favour of customer-centric, experimental and courageous work that people truly love. If you're a product person, you know it's not just about the features. It's about building somethi...

Updated May 17, 2024 to fit M* architecture My favorite development environment on my MacBook includes an improved Zsh shell and an extended .vimrc configuration file with syntax highlighting, error checking, TextMate snippets, and the Solarized color scheme.  Here's a guide for setting up similar features:  The features include directional key navigation for directories and files, developer-friendly colors, command highlighting, improved history search, auto-complete for options and SSH connections (if keys are known), and many more useful enhancements.   Get Xcode:  AppStore => Xcode => Install Xcode From now we use a terminal window. Install Brew /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" Install git and wget:   brew install git   brew install wget Install oh-my-zsh:   wget --no-check-certificate https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -O - | s...

This piece explains how generative AI has become a genuine accelerator for product ideation after years of underwhelming brainstorming tools. Instead of chaotic workshops dominated by loud voices or stale thinking, modern multimodal models like GPT-4o can surface unconventional ideas, emulate customer reactions, analyse markets, and help prototype concepts rapidly. Used well, AI becomes an “unfair advantage” for founders and product leaders by expanding creative range, grounding ideas in data, and sharpening strategic decisions. The article outlines a practical workflow — defining the problem, generating solutions, prototyping, and running devil’s-advocate analyses — plus a role-play method used successfully with Apache Wayang. The takeaway: AI doesn’t replace human judgment, but in a mature XOps culture it becomes a force multiplier that helps teams think clearer, move faster, and build better products. I like product ideation brainstorming—done right and focused,  it opens my ...

This article reframes XOps as a practical, collaborative mindset rather than another corporate buzzword. Drawing on experience as a CPO, it argues that breaking down silos between product, design, data and sales is essential for building user-centric products, especially in greenfield environments where culture forms early. XOps provides the glue: shared outcomes, cross-functional learning, data-driven decisions and fast experimentation. The piece outlines how each role contributes to a unified product engine, why collaboration reduces wasted effort and accelerates time-to-market, and how flexible deployment models—from embedded ProductOps to external consultants—help teams adopt the approach. The takeaway is clear: XOps succeeds only when leaders drive cultural change, champion honest feedback and reward collective impact. Done right, it becomes a strategic advantage that speeds delivery, reduces waste and creates happier, high-performing teams. First: XOps is not a new Marvell mov...

This article argues that most product definitions fail because they obsess over features instead of clarifying the real customer problem and the product’s unfair advantage — the core strategic edge competitors can’t easily copy. It lays out a practical approach: start by understanding the “why” through deep user insight and simplicity of explanation, then build a Minimum Viable Definition that focuses on problem–solution fit, ease of use and data-backed validation. Early, low-fidelity feedback loops help avoid building in isolation, while prioritisation frameworks like MoSCoW ensure focus on what truly matters. The takeaway: strong product definitions are living, customer-centric documents built on clarity, evidence and a unique strategic edge that turns simple ideas into scalable products. Let's be honest, most product definitions suck. They're either packed with jargon that makes your eyes glaze over, filled with features nobody gives a crap about, or so vague t...

This article dismantles the industry’s obsession with “engagement” and argues that most churn problems stem not from customers failing to understand a product, but from companies failing to understand their customers. It calls out dark patterns, noisy notifications, gimmicks and freemium traps as symptoms of a metrics-driven culture that ignores actual human experience. Instead, it promotes user disengagement as a strategic advantage: building products that quietly solve real problems, respect time and privacy, and integrate so smoothly into users’ routines that they stay because life is easier with the product than without it. The piece outlines a practical philosophy—measure outcomes, not screen time; design graceful exits; give users control; remove manipulative friction; and build loyalty through clarity and usefulness rather than tricks. The result is healthier teams, happier customers and products that succeed by being genuinely valuable, not endlessly attention-seeking. Last ...

This article distills years of experience moving from engineering into product leadership into a simple truth: great products win because they obsess over customer experience, not feature volume. It argues against feature-churn and blind adherence to rigid Agile rituals, advocating instead for a hybrid model where Kanban provides flow, focus and instant responsiveness while Agile supplies structure and iteration. The core message is a mindset shift—deep research, mapping customer journeys, validating data, asking “why” relentlessly and prioritizing with discipline through frameworks like MoSCoW. Combined, these practices help teams build products that solve real problems so effectively that they become irreplaceable in users’ routines. The result is faster learning, stronger differentiation, and a product culture capable of sustaining long-term growth. I've been building products for a long time now, moving from Solution Engineer and Solution Architect over Product Manager to my...