Training & Development

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. [Truncated for length. Full text: https://lnkd.in/gKDQ6H9s]

If you could invest in one thing to live a longer, healthier, and happier life, what would it be? For more than 85 years, researchers at Harvard followed hundreds of people across their entire lives, tracking everything from their physical health and mental well-being to their careers, relationships, and aging. It became the longest-running study of adult development in history. After decades of data, one finding stood above the rest: The strongest predictor of a long, healthy, and fulfilling life wasn't wealth, fame, social status, cholesterol levels, or genetics. It was the quality of your relationships. The people who were most connected to family, friends, and community lived longer, stayed healthier, and were happier as they aged. In fact, participants who were most satisfied in their relationships at age 50 were the healthiest at age 80. The researchers also found that close relationships helped protect the brain. People with strong, supportive connections experienced less cognitive decline and maintained better memory and mental function later in life. Investing in meaningful relationships may be one of the most powerful things you can do for your longevity, happiness, and overall well-being.

Wars don’t just destroy nations. They expose how fragile our systems really are. Over the past few years, every global disruption, from conflicts to pandemics to supply shocks, has shown us one thing clearly: We have built a world that is highly efficient… but dangerously dependent. - Food travels thousands of kilometres before it reaches our plates. - Energy systems rely on distant, unstable sources. - Waste is exported, outsourced, and forgotten. And the moment something breaks somewhere in the world, everyone, everywhere, feels it. Maybe the question isn’t: How do we make global systems stronger? Maybe the question is: Why are we so dependent on them in the first place? And what if our cities, towns and villages could: • Grow more of their own food • Generate more of their own energy • Manage their own waste • Create and consume locally This isn’t about isolation. It’s about resilience. Because when systems are decentralised: • Communities recover faster • Livelihoods are created locally • Environmental impact reduces • And people regain a sense of ownership This is where sustainability meets survival. Decentralised production systems are not just a climate solution. They are a risk mitigation strategy for an uncertain world. The future isn’t global vs local. It’s global and local. In fact, its hyperlocal. But the balance has clearly tipped too far. If there’s one lesson from the world we’re witnessing today, it’s this: The strongest communities are the least dependent ones. Time to build local. Time to act resilient. Time to rethink how we produce, consume, and live. What do you think? #Decentralisation #Sustainability #Resilience #ClimateAction #LocalEconomies #CircularEconomy

The Department of Labor released an AI Literacy Framework today, and it's really encouraging to see that the approach aligns closely with the work we're doing at AI for Education. The framework defines AI literacy with these foundational content areas: - Understanding AI principles - Exploring AI uses - Directing AI effectively - Evaluating outputs - Using AI responsibly What's notable is that the framework addresses both content and delivery, which we think about all the time. Beyond the foundational content areas, it emphasizes effective delivery principles like experiential learning, building agility, and developing complementary human skills. We also like that the framework doesn't focus primarily on risks or displacement. Instead, it positions AI as a tool that can support workers and open new opportunities when people have the right skills and support. It acknowledges real concerns about AI's workforce impact while focusing on actionable preparation. It's encouraging to see the DOL support the kind of practical, human-centered AI literacy work that we have seen work with our partners. You can find the link to the full framework in the comments. Let us know what you think. #AILiteracy #FutureOfWork #AIinEducation

Most people are taught how to be high performers. But too few are taught how to perform in a team. And that’s a problem, because in most roles, you’re not an individual contributor. You’re part of a larger entity, working with others to build something. Yet, I see founders spend hours refining their product or systems,  But don't devote time to team development. At HomeServe, I approached team performance with purpose,  And it was one of the best decisions I made. Here are 7 tools I’ve used (and still use) to build high-performing teams,  Based on real lessons from building a £4.1bn business: 1️⃣ Start With Why (Simon Sinek) ↳ Before you focus on what or how...get clear on why. WHAT – The product you sell or the service you provide HOW – What makes you different WHY – Your deeper purpose or belief Every great team needs a reason to get out of bed in the morning. 2️⃣ The 70-20-10 Rule (McCall, Lombardo & Eichinger) ↳ How people actually learn on the job: 70% from challenging experiences 20% from coaching and mentoring 10% from formal training Most teams over-invest in training, and under-invest in real development. I'm amazed at how few founders or CEOs have a coach or mentor. 3️⃣ The Trust Triangle (Frances Frei, Harvard) ↳ Trust isn’t built with perks. It’s earned in three ways: Authenticity – Are you real? Logic – Do your decisions make sense? Empathy – Do you care? Without trust, you can’t build speed or loyalty. 4️⃣ The 5 Stages of Team Development (Tuckman Model) 1. Forming – Team gets together 2. Storming – Conflicts surface 3. Norming – Ground rules form 4. Performing – Results roll in 5. Adjourning – Project ends or evolves Don't panic during ‘storming’. It’s necessary friction. 5️⃣ The Johari Window (Luft & Ingham) ↳ Self-awareness is a team sport. Open – You know, they know Hidden – You know, they don’t Blind Spot – They know, you don’t Unknown – No one knows (yet) This helps surface feedback, build confidence, and avoid surprises. 6️⃣ The Energy/Impact Matrix (Inspired by McKinsey) ↳ Map every team member’s impact vs. energy. Use it to: Make smart hiring/firing decisions Spot burnout early Retain high performers High-performing teams don’t tolerate drift. 7️⃣ The RAPID Decision-Making Model (Bain & Company) ↳ High-performing teams make fast, clear decisions. Recommend – Suggest the course of action Agree – Those who must sign off Perform – Executes the decision Input – Provides relevant facts or opinions Decide – Final decision-maker This clears up delays, dropped balls, and blame. Building a great team is about building an environment where talent can actually thrive. I go deeper into team-building in my new book. Order it today: https://lnkd.in/eRYDKXdT ♻️ Repost if you believe team performance should be built, not assumed. And for more on how I scaled teams to build a £4.1bn business, Follow me Richard Harpin.

Stress isn’t always about the thing itself. It’s about our relationship to it. Two leaders can face the exact same challenge — a missed deadline, a difficult board meeting, a team conflict — yet their experience of stress is entirely different. Why? Stress often has less to do with the external event and more to do with the lens through which we view it. 👉 When we label something as unbearable, it grows heavier. 👉 When we approach it as a problem to be solved, it becomes manageable. 👉 When we see it as an opportunity to grow, it can even become empowering. This distinction matters because leaders carry tremendous weight. If everything feels like a “threat,” stress compounds. But if we learn to reframe — to shift our relationship to the pressure — we not only reduce stress, we increase our capacity to lead with clarity and resilience. As an executive coach, I work with clients on this every day. Here are a few practices that make a difference: ✅ Name it clearly. → Is it the situation itself that’s stressful, or the meaning you’ve attached to it? Naming the difference is the first step in reframing. ✅ Shift the narrative. → Instead of asking “Why is this happening to me?”, try “What is this asking of me as a leader?” ✅ Control the controllable. → Stress escalates when we fixate on what’s outside our power. Refocus on the small actions you can take. ✅ Build in recovery. → Even the strongest leaders need rituals that restore — whether that’s exercise, mindfulness, or simply 10 minutes of stillness. The goal isn’t to eliminate stress. The goal is to reshape our relationship to it so it serves us, rather than overwhelms us. Coaching can help; let's chat. Book Your Coaching Discovery Call Today  ↳ https://lnkd.in/eKi5cCce Enjoy this? ♻️ Repost it to your network and follow Joshua Miller for more tips on coaching, leadership, career + mindset. #executivecoaching #leadership #mentalhealth #coachingtips #wellness

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💚

McKinsey & Company 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟱𝟬+ 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱 𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗿𝗲𝗮𝗱: ⬇️ One-off solutions don’t scale. The most successful projects take a different path: They use open, modular architectures that enable speed, reuse, and control. → Designed for reuse → Able to plug in best-in-class capabilities → Free from vendor lock-in This is the reference architecture McKinsey now recommends — optimized to scale what works while staying compliant. It consists of five core components: ⬇️ 𝟭. 𝗦𝗲𝗹𝗳-𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹: → A secure, compliant “pane of glass” where teams can launch, monitor, and manage GenAI apps. → Preapproved patterns, validated capabilities, shared libraries. → Observability and cost controls built-in. 𝟮. 𝗢𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 → Services are modular, reusable, and provider-agnostic. → Core functions like RAG, chunking, or prompt routing are shared across apps. → Infra and policy as code, built to evolve fast. 𝟯. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 → Every prompt and response is logged, audited, and cost-attributed. → Hallucination detection, PII filters, bias audits — enforced by default. → LLMs accessed only through a centralized AI gateway. 4. 𝗙𝘂𝗹𝗹-𝘀𝘁𝗮𝗰𝗸 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Centralized logging, analytics, and monitoring across all solutions → Built-in lifecycle governance, FinOps, and Responsible AI enforcement → Secure onboarding of use cases and private data controls → Enables policy adherence across infrastructure, models, and apps 5. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 → Modular setup for user interface, business logic, and orchestration → Integrated agents, prompt engineering, and model APIs → Guardrails, feedback systems, and observability built into the solution → Delivered through the AI Gateway for consistent compliance and scale The message is clear: If your GenAI program is stuck, don’t look at the LLM. Look at your platform. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

When Ozan Varol first became a law professor, he’d pause mid-lecture and ask: “Does anyone have any questions?” -Crickets- He assumed he nailed the lesson. But the exam results said otherwise. So he ran an experiment. Instead of asking if anyone had questions, he said: “That was confusing. I’m sure a bunch of you have questions. Now’s the time to ask.” Suddenly—hands shot up. Why did that work? Because it did 3 powerful things: 1. Normalized confusion 2. Created psychological safety 3. Made it feel okay to not “get it” Students weren’t silent because they understood… They were silent because they were scared to speak. This isn’t just for professors. Doctors can say: “I know I used a lot of medical jargon—what questions do you have?” Leaders can say: “That was a tough quarter. I know we’re all facing challenges—what’s come up for you?” People don’t speak up because they don’t want to look weak. Not in front of peers. Not in front of bosses. Not in front of future collaborators. Your job isn’t just to ask questions. It’s to create the conditions for honest answers. The goal isn’t “Who has questions?” The goal is: “How can I make asking questions feel safe, smart, and expected?” Final takeaway from Ozan: Breakthroughs don’t start with smart answers. They start with better questions. Asked the right way. At the right time. To the right people. What’s one question you’ve been asking the wrong way? Reply below—let’s rewrite it together. 👇

I’ll be honest. 
When I first started stepping away from the day-to-day… I used to feel a strange satisfaction when things broke in my absence. 
It made me feel important. 
Like I was the glue holding it all together. But the truth is harsher: 
Every time something breaks when you’re not there, it’s a sign you’ve failed to build a system that works without you. That’s not leadership. 
That’s being a bottleneck. 
A liability. Because when progress depends on your availability, your time, your personal input - the whole business becomes fragile. 
You become the single point of failure. Let me be clear: 
If your team needs you to approve every small move, you’re not scaling excellence - you’re scaling dependence. 
That’s ego. Not leadership. Real leadership is when: - The thinking happens without you. - The decisions happen without you. - The momentum continues without you. Not because you’re not needed. 
But because you’ve built a system that doesn’t collapse when you’re not in the room. If you step away and things grind to a halt, you haven’t built a high-performing team. You’ve built a fragile operation propped up by your control. And that’s on you. Every time something breaks in your absence, it’s feedback: - A system isn’t clear. - Accountability isn’t owned. - Trust isn’t built. It’s a signal to fix the machine, not to double down on micromanaging. Because here’s the harsh reality:
 A business that can’t run without you is a business that can’t grow beyond you. Let that sting. 💡George Stern