Mindset Development Tips

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

I did not do an MBA. And in many ways, that became my biggest competitive advantage. Over the years, I have met many bright young managers from top institutes. Extremely sharp. Very articulate. Very confident. Sometimes a little too confident. There is a tendency to feel that once you have the degree, you know enough. Reading slows down. Curiosity slows down. Real learning slows down. Business does not work like that. It is not only about strategy decks and case studies. It is about people. Culture. Talent. Judgment. These are not subjects you can fully learn in a classroom. And the world is not static. Geopolitics is shifting. Technology is redefining industries. Consumer behaviour changes faster than any curriculum. So you have to keep learning. Continuously. I prefer dialogue over reports. I would rather debate with a thought leader than read a polished consultant presentation. Through interaction, both sides refine their thinking. A report gives you information. Conversation gives you insight. I also believe in going to the ground myself. Meeting teams two levels below. Visiting markets. Listening directly. Not to monitor, but to understand. If I had to summarise the ground level truths no classroom can teach you, they would be: 1. The moment you think you know it all, you stop growing. 2. Strategy without culture and talent will eventually fail. 3. Judgment is built through experience and interaction, not frameworks. 4. Continuous learning is non negotiable in a fast changing world. 5. Dialogue sharpens thinking far more than a static report. 6. Real insight comes from going to the ground, not sitting in a boardroom. A degree can open doors. Humility, curiosity, and the willingness to keep learning keep you relevant. #leadership #mindset #learning

Do you ever find yourself more stressed when thinking about an event than the event itself? That’s ‘anticipatory anxiety.’ It’s like your brain is stuck in a “what if” loop. Most of my clients describe this exact feeling before big presentations, tough conversations or even social events. I’ve been there myself… I used to spend days imagining everything that could go wrong, which only made me feel more overwhelmed and unprepared. Turns out, the usual advice of “Just think positive” doesn’t help at all. Research shows that focusing ONLY on perfect outcomes can actually drain your energy, and make you less likely to achieve it. When your brain skips over the challenges, you’re left less prepared and even more anxious if things don’t go as planned (again, I’ve been there!). What finally worked for me (and our clients) was flipping the script. Acknowledge the worst-case scenario and plan your response: – What’s the backup if the tech fails? – How will you recover if the conversation takes a turn? – What will you do if you forget your script? You’re not necessarily expecting the worst, but being ready for it. The result is a calmer, more confident mindset going into it, and that makes all the difference. P.S. Has someone ever told you to “just visualise success?” How did that work out for you?

I’m 60. After 30 years scaling HomeServe to a £4.1bn exit... Here are 20 lessons I wish I'd known back in my 20s. If you’re building something ambitious, this one’s for you: 1. Copy what works, then make it better ↳ Take what works and improve the execution. 2. Build before you raise ↳ Raise capital when your model works and not when you’re desperate for cash. 3. A coach shows you options for a problem or opportunity. A mentor gives you advice on which route to take and why. ↳ Get both. I call it Coachment. It’s how I help founders today. 4. Hire your replacement early ↳ If the business can’t run without you, it can’t grow beyond you. 5. Test in the real world.  ↳ Real world research and small moves will teach you more than months of planning. 6. You don’t need to be clever, just clear ↳ Customers and teams need simplicity, not complexity. 7. Every business is a sales business ↳ My early days knocking on doors taught me what customers really value. 8. Face-to-face > perfect pitch decks ↳ Authenticity wins more trust than polish, especially with investors. 9. Execution > ideas ↳ Everyone has ideas. Only a few have the discipline to make them happen. 10. Grow fast, but with control ↳ Fast growth needs frameworks and habits to avoid chaos. 11. Customers only care about outcomes ↳ Solve their problem, and you’ll never be short of demand. 12. Culture is built by what you tolerate ↳ Saying nothing when things go wrong sets the wrong tone. 13. Hire slow, fire fast ↳ It sounds harsh, but nothing hurts culture more than keeping the wrong people. 14. Don’t just build a business, build a brand ↳ The trust you create with a brand will compound. 15. Play the long game ↳ Create trust and prioritise longevity over speed. 16. Character beats talent ↳ At HomeServe, I looked for humility, persistence, and resilience over pure IQ. 17. Make hard calls early ↳ If something feels off, deal with it before it becomes a bigger problem. 18. Ask better questions ↳ Coaching works best when it creates the space to help people think, not just react. 19. Say no to good ideas to protect great ones ↳ I've turned down tempting side projects to focus on my core mission. 20. Stick with it long enough to get lucky ↳ Most “overnight successes” are 10+ years in the making. I wish I had this list on my desk when I was 25.  It would have reduced all the mistakes I made. Which one of these lessons stands out to you? 👇 I'd like to hear which one resonated most. ♻️ Repost to share these lessons with your network,  And for more on what it takes to build and scale businesses,  Follow me Richard Harpin.

A solo business used to mean choosing between freedom and real money. In 2026, you can have both. The tools are cheaper, the audience is bigger, and one focused person can do what used to take ten. Most people CAN do it. But getting started is the hardest part. What they're missing is a clear system to follow. So I wrote one you can use: 1/ Solve Your Own Pain The easiest business to build is around a problem you actually have. Find the solution you'd pay for yourself. 2/ Validate Before Building Talk to 10 people who have the same problem before you spend time and money on any idea. Ask what they've already tried and whether they'd pay to solve it. Most people skip this and build for 3 months in the wrong direction. 3/ Build Your Personal Brand Your online presence can give you a serious advantage. It makes people feel like they know you before you even approach them. Pick one topic you know well, write about buyer problems, and use personal stories and case studies to prove you can help. 4/ Build An Offer Ladder Your first product can be one free lead magnet. Solve a narrow, painful problem with a fast win. Then build up from there. A checklist or template that saves someone an hour is worth more than a 40-page ebook nobody finishes. 5/ Use AI Wisely Use AI to draft, research, and organize - NOT to decide what to build or who to sell to. The strategy has to come from you. The execution is where AI can help. 6/ Ship The Simplest Version The first version only has one job: prove people care enough to pay. Start with something simple to build and maintain like a checklist or email course before building anything more complex and expensive. 7/ Make Buying Easier People buy when the result feels clear, fast, and low-risk. "First lead in week 1" converts better than "grow your business." 8/ Say No Often A one-person business only works if that person is maniacally focused on the main goal. Every yes to something outside your focus is a no to the thing that's actually working. A one-person business lives and dies by focus. Do fewer things. Do them longer. 📌 Want a high-res version of this sheet? Get it here: https://lnkd.in/gKzZUq-b ♻️ Repost to help your network build a one person business. ➕ Follow me (Will McTighe) for more like this.

Everyone should be using Claude Code more PMs, marketers, designers, founders, parents. Everyone. The trick is to forget that it’s called Claude Code and instead think of it as Claude Local or Claude Agent. It’s essentially a super-intelligent AI running locally, able to do stuff directly on your computer—from organizing your files and folders to brainstorming domain names, summarizing customer calls, to enhancing image quality, creating Linear tickets, and so much more. Here are 50 creative ways non-technical people are using Claude Code in their work and life, to inspire your own thinking. This list includes my own favorite use cases, and the best examples y’all shared with me: 1. Finding high-quality leads, from Jeff Lindquist “I literally just typed: look at what I’m building and identify the top 5 companies in my area that would be good for a pilot for this. Then I go to LinkedIn and message them. If it’s not clear, I do this in the source directory of the code of my app so the first thing it does is figure out what it is that I’m building.” 2. Figuring out why your computer is running slow, from Anthony Roux “I sometimes use Claude Code for system diagnostics when my Mac slows down. I use it to check load averages, memory pressure, disk space, stuck processes, and swap activity, then it dives deeper to find what’s actually causing issues. It can calculate cache sizes, check Docker usage, find Time Machine snapshots eating space, etc." 3. Noticing when you’re avoiding conflict, from Dan Shipper “I download all of my meeting recordings, put them in a folder, and ask Claude Code to tell me all of the times I’ve subtly avoided conflict.” 4. Clearing space on my computer Prompt: “How can I clear some storage on my computer?” I then discuss my options. 5. Brainstorming domain names, from Ben Aiad “Just describe your project, and it’ll suggest creative options across multiple TLDs (.com, .io, .dev, etc.) while verifying what’s actually available to register.” 6. Improving the image quality of screenshots Prompt: “Improve the image quality of [filename]”. I used this many times for the screenshots in this post. 7. Downloading YouTube videos Prompt: “Download this YouTube video: [URL]”. Then I ignored all the warnings 🤫 8. Downloading all of the images embedded inside a Google Doc Prompt: “Download all of the images in high-res from this Google Doc: [URL]”. This paired well with item #2. 9. Cleaning up messy invoice files, from Martin Merschroth “I use Claude Code to sort my invoices for taxes. It reads each file in a messy folder, renames it to ‘YYYY-MM-DD Vendor - Invoice - ProductOrService.pdf’, and moves it into the right folder.” Here's the whole collection (bookmark it for later), including a quick 3-step guide to installing Claude Code so you can try this stuff at home: https://lnkd.in/g_wqdx36

What I Look for Before I Invest In A Business Having sat on both sides of the table, as a founder seeking funds and as an angel investor (and Shark 🦈) deploying funds: I’ve realized that when I invest in a startup, I’m evaluating far more than just the pitch deck numbers. Here’s what I really look for when a founder pitches to me: ☑️ Passion & Resilience: I know, everyone says “I’m passionate,” so how do I gauge it? By the sparkle in their eyes when they talk about their product and the honesty when discussing hurdles. I often ask, “What will you do if things don’t go as planned?” – a well-thought answer here shows me they’re in it for the long haul, not just the glory days. ☑️ Understanding of Customers: You’d be surprised how many pitches focus on market size but gloss over the actual customer. I love hearing a founder say, “I spoke to 100 potential users and here’s what they said.” It shows me they’re grounded and customer-obsessed. If you know your users deeply, you can pivot and iterate intelligently. ☑️ Coachability: No one has all the answers, and that’s okay. I actually appreciate when a founder says “I don’t know” and follows up with “…but I’m eager to learn or get help.” It tells me they’re open to mentorship and collaboration. An investee-investor relationship is like a partnership – I don’t want to just write a check; I want to add value. It’s easiest to help someone who’s receptive to feedback and new ideas. ☑️ Alignment of Values: This one is more intangible, but crucial. I check – does this founder’s ethos align with mine? For example, if a founder is willing to compromise on product safety or ethics for a quick buck, we’re probably not a fit. But if they demonstrate integrity (even in small anecdotes, like how they handled a customer complaint), that builds trust. I invested in one startup mainly because the founder said their first big purchase order was delayed and they chose to be transparent with clients rather than cover it up. That honesty won me over. In short, I invest in the person as much as the business. The right investor-founder fit is like finding a co-founder. So if you’re pitching (to me or anyone), remember: beyond the TAMs and P&Ls, we’re listening for your story, character, and vision. And as always, if you have a pitch that fits my areas (D2C, sustainability, etc.), I’m all ears. What qualities do you value most in a founder or an investor? #Leadership #StartupFunding #AngelInvestor #Entrepreneurship

I taught myself machine learning > 10 years ago. If I had to start again today, I wouldn’t touch models, LLMs, or agents first, as many AI experts suggest. I'd start with the math and the code. Ugly truth: 90% of people skip the foundations, then wonder why everything feels like magic or falls apart in production. If you want to be different, actually understand ML, not just copy-paste, this is the roadmap I'd follow: Start with fundamentals: Because no matter how fast LLMs or GenAI evolve, your math, code, and logic will keep you relevant. Here's what you should focus on: 📐 1. Linear Algebra Learn these core ideas: Vectors, matrices, tensors Matrix multiplication (dot products, broadcasting) Transpose, inverse, rank, determinants Eigenvalues & eigenvectors (especially for PCA & embeddings) Projections and orthogonality ✅ Use NumPy to implement everything yourself → Practice matrix ops, dot products, and visualizing transformations with Matplotlib 🔁 2. Calculus Focus on: Derivatives & partial derivatives Chain rule (for backpropagation in neural nets) Gradient descent Convex functions, minima/maxima ✅ Use SymPy or JAX to visualize and compute derivatives → Plot functions and their gradients to develop deep intuition 🎲 3. Probability You need a solid grip on: Random variables (discrete & continuous) Conditional probability & Bayes' rule Joint & marginal probability The Chain rule Expectation, variance, entropy Common distributions: Bernoulli, Binomial, Gaussian, Poisson Central limit theorem The law of large numbers ✅ Simulate simple probability experiments in Python with NumPy → E.g. simulate sampling from distributions 📊 4. Statistics These are must-know topics: Descriptive stats: mean, median, mode, standard deviation Hypothesis testing: p-values, confidence intervals, t-tests Correlation vs. causation Sampling, bias, and variance Overfitting/underfitting A/B testing basics ✅ Use Pandas & SciPy to explore real datasets → Calculate descriptive stats, create histograms/box plots, run t-tests 🔧 Essential Python libraries to learn early NumPy – for vectorized math and fast array ops Pandas – for loading, cleaning, and analyzing tabular data Matplotlib / Seaborn – for plotting and visualizing distributions, relationships, and trends SymPy – for symbolic math and calculus SciPy – for stats, optimization, and numerical methods Use Jupyter Notebooks(to combine math, code, & visuals in one place) 📚 Best resources to nail the fundamentals: ✅ Machine Learning Foundations Math series (ML Foundations: Linear Algebra, Calculus, Probability, and Statistics)-series of 4 courses that I've created together with LinkedIn learning ✅ Hands-On ML with TensorFlow & Keras book by Aurélien Géron ✅ The Hundred-page Machine Learning Book by Andriy Burkov If you want to become an actual ML engineer, not just someone who watches and copies demos, start here. ♻️ Repost to help others💚

Building a venture or startup is a whole different journey compared to running a small business. While small businesses focus on steady income and serving local communities, startups aim for rapid growth, scale, and often, disruption at a much larger scale. Take Amazon’s journey, for instance: what began as a small online bookstore quickly grew with an ambitious vision to become the "everything store," using bold innovation and risk-taking to transform itself into a global giant. Ventures thrive on big risks and bold strategies, requiring unique resources and mindsets. Treating a startup like a small business can hold it back, so it’s crucial to approach each with the right tools, vision, and goals to truly make an impact.

If I had to start DSA all over again from scratch… here’s exactly what I’d do. 1) Pick a Language (and stick to it) I chose C++ because it’s fast, widely used in competitive programming. If you already know Java or Python, that’s fine too. The important part? Don’t keep switching languages. But I will recommend c++ 2) Start with simple problems Begin with arrays, strings, and basic maths problems. The goal here is to get comfortable with problem statements, not to solve the hardest problems on day 1. Try platforms like HackerRank, or LeetCode Easy section. 3) Move topic by topic One topic at a time. My flow was- Arrays → Strings → Recursion → Linked List → Stack & Queue → Trees → Graphs → Dynamic Programming. Don’t jump randomly, it kills consistency. 4)Striver’s DSA Sheet ( I followed) When you’re done with basics, start Striver’s DSA Sheet and try to solve it in order. It’s a mix of easy, medium, and hard problems that cover almost everything for placements. If you get stuck, watch editorials and solutions written by other coders but don’t copy-paste without understanding. 5) Watch this video once -Fastest Way to Learn Data Structure and Algorithm by Sahil & Sara on YouTube. Trust me it’s a game changer 6) Consistency > Motivation Even 1 hour a day is better than a random 5-hour in one day in whole week. Trust me don’t give gaps. If you want the exact resources I followed (roadmaps, YouTube playlists, and my notes), drop your email in the comments, I’ll share them with you. You just need to start… and then keep going. From arrays to DP, one problem at a time