Training & Development

𝗖𝗹𝗲𝗮𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. I came across a video about why clear documentation matters, and although it was lighthearted, it captured a challenge I've seen repeatedly while working with organizations. Most teams believe they have documented their processes. What they've often documented are the steps, while leaving out the judgment behind them. Experienced people fill in those gaps automatically because they know which shortcuts to take, which exceptions to ignore, and which decisions require extra attention. New colleagues don't have that advantage, which is why the same document that looks perfectly clear to one person can be frustratingly incomplete to another. 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗼𝗻𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺, 𝗶𝘁 𝗶𝘀𝗻'𝘁 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. 𝗜𝘁'𝘀 𝘁𝗿𝗶𝗯𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. The strongest organizations don't become resilient because they hire exceptional people. They become resilient because exceptional people make their knowledge transferable. They create documentation that explains not only what to do, but also why it matters, when to adapt, and how success should be measured. That's becoming an increasingly valuable leadership skill because every new hire, every cross-functional project, and every technology initiative depends on the same foundation. Clarity. 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘀𝘁𝗶𝗹𝗹 𝗿𝗲𝗹𝘆 𝗼𝗻 𝘁𝗿𝗶𝗯𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗰𝗹𝗲𝗮𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻? #Leadership #KnowledgeManagement #FutureOfWork #Management #Communication Source: Noelidavid

“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]

The most consequential decisions in a career are often the ones that look irrational in the moment. A common pattern in high-growth companies is that the most impactful roles are often the least defined at the outset. The title is unclear. The scope is fluid. By traditional metrics, it can look like a step down. That is the point. Early in your career, and often well into it, people optimize for position. They evaluate title, compensation, reporting lines. They try to map a linear path forward. This is a legacy framework from a more static economy. But in periods of technological acceleration, the variables that matter shift. High-growth companies compress time and push you beyond your prior experience. They push you to develop new skills quickly and operate beyond your prior experience. One year of work can feel like five. In those conditions, the job description becomes secondary. What matters is whether you are working on important problems alongside people who raise your standard. Careers tend to follow momentum. The environments you choose shape the trajectory more than the plans you start with. The challenge is that these opportunities rarely present themselves clearly. They look incomplete. Uneven. Risky. The question is whether you can recognize directional momentum early and commit before the outcome is fully defined. #schmidtsights

I put together a new LLM Architecture Gallery that collects the architecture figures I shared over the months and years in one place. The goal is to make it easier to quickly browse recent open-weight LLM architectures without jumping back and forth between article sections. I also added dates, config.json links, tech-report links, and from-scratch implementation links where available for easy reference, alongside short concept explainers for things like GQA, MLA, SWA, QK-Norm, NoPE, Gated DeltaNet, and related ideas. You can find it here: https://lnkd.in/gJX7EEGv Happy browsing!

India’s green economy is growing fast but LinkedIn data suggests green talent is growing even faster. The LinkedIn Hiring Rate (LHR) for green talent — defined as professionals with green skills, green job titles, or both — is now 59.7% higher than for the overall workforce. This means green-skilled professionals are significantly more likely to be hired than their peers, underscoring the growing demand for sustainability-focused roles. “The prioritisation of green talent by Indian companies is being fuelled by an interplay of policy reforms, rising consumer consciousness, and the need for deep business transformation,” says Neelima Burra, Chief Strategy, Transformation, and Marketing Officer at Luminous Power Technologies. “Government initiatives like the PM Suryaghar Yojna, National Solar Mission, and Smart City Mission, combined with the growing mandate for ESG reporting — are also pushing companies to recruit sustainability experts, carbon auditors, and ESG strategists to meet regulatory and investor expectations,” she adds further. Operational efficiency has emerged as the top skill across the top five industries increasingly hiring for green skills, as per LinkedIn data. In contrast, precision agriculture skills lead in farming, ranching, and forestry — highlighting how sector-specific green skills are evolving. “Operational efficiency offers the fastest route to tangible returns. It moves the conversation beyond regulatory compliance to net profitability, ensuring we can do more with less energy and fewer materials,” says Venu Nuguri Managing Director and CEO at Hitachi Energy. This surge in demand aligns with broader economic trends. Green jobs in India have grown over 10 times in the past five years, with Gen Z accounting for 63% of applicants, reports The Economic Times, citing a report by WeNaturalists. The projections are equally ambitious. India’s green economy will generate 7.29 million jobs by FY28 and 35 million by 2047, as the sector scales toward a $1 trillion valuation by 2030 and $15 trillion by 2070, suggests another report by The Economic Times, citing a report by NLB Services. The message is clear: green skills aren’t just good for the planet — they’re becoming essential for employability. As India accelerates its climate and economic goals, the workforce is already adapting. The question now is whether education, training, and policy can keep pace. Read the full report here: https://lnkd.in/g873CzHT #COP30 #GreenerTogether Source: The Economic Times: https://lnkd.in/d-3bShQP  The Economic Times: https://lnkd.in/dSUMFS58 

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.

You don't build culture by offering free snacks or a gym membership. You build culture by talking to people and understanding what they care about. Leaders work for their employees, not the other way around. That means you have to understand what your employees want at a deep level. You have to be constantly adapting to their needs and what they want from the organization. Listening to employees, having one-on-one meetings, catering to what they want, taking the blame, and being the bigger person... that's how you win.

The electronics industry is adding jobs faster than ever, yet we’re still facing a talent gap at every level. 💡 The challenge? There’s no one-size-fits-all pathway into our field. Some roles can be learned through hands-on experience. Others require advanced degrees. Most demand a combination of both, and a commitment to continuous learning. I shared with Alison Griffin at Forbes about how the Global Electronics Association is helping build flexible, resilient talent pipelines by: 🔹 Aligning training with real industry needs 🔹 Connecting employers, educators, and policymakers 🔹 Supporting skill-building from high school to corporate academies Colorado is proving what’s possible when these pieces come together. Their multi-pathway approach, apprenticeships, degree programs, and corporate training aren’t just filling jobs. It’s creating lasting careers and stronger communities. Better pathways mean better opportunities. And in $4.5 trillion electronics industry, that benefits us all. Read the full story: http://bit.ly/3UsTl37

If you're a founder trying to fundraise right now, it probably feels like the entire venture world has gone quiet. The response times are slow, OOOs are on and it’s easy to feel like you’re losing momentum. Don't stress. The summer slowdown is predictable, and it's not a setback, it's a gift of time if you use it well. I see this every year... The founders who scramble to send frantic emails in July/August are the same ones who struggle in the fall with an over-shopped deal and the fatigue of an endless fundraise. But the founders who use this quiet period for deep, focused preparation are the ones who run a crisp, successful process after Labor Day. The fundraising race is won in the prep lap. Here are a few things you can do right now to prep for a big fundraising push this fall: 1. Build a High-Fidelity Investor Pipeline. Go beyond a simple list of names. Create a comprehensive document that tracks every firm and partner, their specific thesis, your history with them (if any), your connections to them and crucially, the feedback they've given you in the past. This turns your outreach into a strategic campaign. 2. Assemble a "Push-Button" Data Room. Don't wait for an investor to ask. Build your data room now so it's ready to go at a moment's notice. This includes your customer contracts, cohort analyses, deck, references and financial model. A well-organized data room signals professionalism and creates momentum. 3. Craft a "Juicy" Forwardable Blurb. The best introductions are easy to forward. Write a tight, compelling, one-paragraph teaser. It must include a unique insight on the market, why your team is going to win and any key metrics. This makes it effortless for people like me to advocate on your behalf. 4. Pressure-Test Your Narrative. Use this time to pitch trusted advisors, mentors, and other founders. This isn't about memorizing a script, it's about finding the weak spots in your story. Ask them to be ruthless. The tough questions you answer now in a friendly setting will save you in a rapid fire partner meeting later. 5. Get Your "Diligence" in Order. This is the one everyone forgets. Talk to your lawyer now. Make sure your corporate governance is tight and your cap table is accurate (and clean). Uncovering a messy problems during late-stage diligence can kill a deal. Solving it now is a massive de-risking event. 6. "Warm Up" Your References. Your best customers are your most powerful asset. Don't wait until an investor asks for a reference call to talk to them. Re-engage with your top 3-5 champions now. Check in, share your progress, and get them excited about your vision. A reference who is prepped and genuinely enthusiastic is infinitely more impactful. The fall fundraising season will be here before you know it. The work you do in the quiet of August will determine the success you have in the chaos of the fall. We are prepping for our next fundraise as well so this is how I'm spending my time💥

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💚