Continuous Learning Practices

It’s simple math 🧐 I use to think that motivation was the key to monumental success. Long story short, it’s not. It’s about the little things you do every day that will take you from reasonable to slightly unreasonable to completely unreasonable progress. Your future is not defined by how motivated you are, but by your daily routines and systems. I believe in this so much that we named our company Butterfly 3ffect to reflect the value of incremental gains. we believe that that’s how the best people and brands grow. Here’s how you grow the small way: 1. Start by setting achievable goals, like reading one chapter of a book each day or going for a short walk 2. Practice gratitude by writing down three things you're thankful for every night before bed 3. Engage in daily self-reflection, even if it's just for a few minutes, to assess your thoughts and actions 4. Incorporate small acts of kindness into your daily routine, like holding the door for someone or offering a genuine compliment 5. Learn something new every day, whether it's a fun fact, a new word, or a new skill 6. Prioritise self-care by getting enough sleep, staying hydrated, and taking breaks when needed 7. Surround yourself with positive influences, whether it's uplifting books, supportive friends, or inspiring podcasts 8. Embrace failure as a learning opportunity and a stepping stone to growth 9. Stay consistent and patient, knowing that small progress over time adds up to significant improvement 10. Celebrate your achievements, no matter how small, to stay motivated and encouraged along the way.

A learning culture is not built by offering more training. It emerges where curiosity, connection, and purpose intersect. Andrew Barry, in The Curious Lion, describes learning culture as a lotus where several forces overlap. I find this framing helpful because it moves the conversation beyond HR programs and into the fabric of the organization. At the individual level, there is curiosity. People must feel invited to ask questions, challenge assumptions, and explore. Without individual curiosity, learning remains compliance. At the organizational level, there is mission. Learning needs direction. When people understand what the company stands for and where it is going, their curiosity becomes focused rather than scattered. At the relational level, there is human connection. Learning accelerates in environments where people feel safe to speak, experiment, and reflect together. The fourth circle is continuous learning. Learning must be ongoing, not episodic. Not a workshop, but a way of operating. Continuous learning ensures that curiosity, mission, and connection reinforce each other over time rather than fading after the latest initiative. When these circles overlap, deeper elements emerge: Shared vision aligns effort. Shared experiences create collective memory. Shared assumptions shape how reality is interpreted. Shared stories transmit meaning across generations. At the center sits what we call learning culture. Not an initiative, but a pattern of how people think, relate, and evolve together. The question for leaders is not, “Do we offer learning opportunities?” It is, “Do curiosity, mission, and connection truly reinforce each other continuously in our organization?” That is where learning becomes cultural rather than occasional.

NVIDIA just exposed the dirty secret about LLMs. A new research paper from NVIDIA shows what many suspected: 👉 Small Language Models (SLMs) can outperform massive LLMs in real-world applications. This flips the current AI playbook on its head. For years, every agentic task — no matter how simple — has been run through massive models like GPT-4 or Claude. NVIDIA’s findings? That approach is wasteful, unnecessary, and about to change. I have a few takeaways that will change how we build AI agents: SLMs are fast, cheap, and effective. Tasks like summarizing docs, extracting info, writing templates, or calling APIs are predictable. For these, SLMs aren’t just “good enough” — they’re better. Smaller ≠ weaker. • Toolformer (6.7B) beats GPT-3 (175B) on API use. • DeepSeek-R1-Distill (7B) outperforms Claude 3.5 and GPT-4o on reasoning. Efficiency is unmatched. • 10–30x cheaper to run • Lower energy use • Faster response times • Easy to deploy locally They’re easy to fine-tune. Techniques like LoRA and QLoRA make overnight customization possible without GPU farms. Perfect fit for structured outputs. SLMs align better with strict formats (JSON, XML, Python) — ideal for agents that need reliability instead of creativity. So why keep running everything through massive LLMs? The smarter path is modular agents: • Default to SLMs. • Call an LLM only when absolutely necessary. This architecture is cheaper, faster, and more controllable. The paper even outlines the migration path: 1. Log usage data 2. Cluster tasks 3. Fine-tune SLMs 4. Replace LLM calls 5. Iterate Why hasn’t the industry switched yet? • Heavy sunk costs in LLM infrastructure • Benchmarks biased toward general tasks • Lack of attention on SLMs But none of these are technical blockers. The future of AI agents isn’t bigger models. It’s smarter architecture. SLMs give you control, speed, and affordability. The paper is worth a read for those at the application layer of AI. Read NVIDIA’s full paper here: https://lnkd.in/gdQRYxyw

People are suffering—yet many still deny that hours with ChatGPT reshape how we focus, create and critique. A new MIT study, “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay-Writing,” offers clear neurological evidence that the denial is misplaced. Read the study (lengthy but far more enjoyable than a conventional manuscript, with a dedicated TL;DR and a summarizing table for the LLM): https://lnkd.in/g6PBVwVe 🧠 What the researchers did - Fifty-four students wrote SAT-style essays across four sessions while high-density EEG tracked information flow among 32 brain regions. - Three tools were compared: no aid (“Brain-only”), Google search, and GPT-4o. - In Session 4 the groups were flipped: students who had written unaided now rewrote with GPT (Brain→LLM), while habitual GPT users had to write solo (LLM→Brain). ⚡ Key findings - Creativity offloaded, networks dimmed. Pure GPT use produced the weakest fronto-parietal and temporal connectivity of all conditions, signalling lighter executive control and shallower semantic processing. - Order matters. When students first wrestled with ideas on their own and then revised with GPT, brain-wide connectivity surged and exceeded every earlier GPT session. Conversely, writers who began with GPT and later worked without it showed the lowest coordination and leaned on GPT-favoured vocabulary, making their essays linguistically bland despite high grades. - Memory and ownership collapse. In their very first GPT session, none of the AI-assisted writers could quote a sentence they had just penned, whereas almost every solo writer could; the deficit persisted even after practice. - Cognitive debt accumulates. Repeated GPT use narrowed topic exploration and diversity; when AI crutches were removed, writers struggled to recover the breadth and depth of earlier human-only work. 🌱 So what? The study frames this tradeoff as cognitive debt: convenience today taxes our ability to learn, remember, and think later. Critically, the order of tool use matters. Starting with one’s ideas and then layering AI support can keep neural circuits firing on all cylinders, while starting with AI may stunt the networks that make creativity and critical reasoning uniquely human. 🤔 Where does that leave creativity? If AI drafts faster than we can think, our value shifts from typing first passes to deciding which ideas matter, why they matter, and when to switch the autopilot off. Hybrid routines—alternate tools-free phases with AI phases—may give us the best of both worlds: speed without surrendering cognitive agency. Further reading: Lively discussion (debate) between neuroethicist Nita Farahany and CEO of The Atlantic, Nicholas Thompson, “The Most Interesting Thing in AI” podcast. The big (and maybe the final) question for us is: What is humanity when AI takes over all the creative processes? Podcast link: https://lnkd.in/emeQkcK6

Your most valuable skill isn't what you know. It's how quickly you learn what you don’t. Many get stuck in their careers by mistaking time for expertise. Years of experience mean little if you stop growing, challenging yourself, and embracing new ideas. It happened to me. Don't let that be you. Here are five principles to keep your career moving forward: 1. Embrace the beginner's mindset. Even as you gain experience, stay humble and curious. Always be open to new ideas. → Ask questions. → Challenge assumptions. → Be open minded not jaded (seriously). The most successful people never stop learning. 2. Make learning a daily habit. Don't rely on your company to teach you (spoiler: they won't). → Block out focused learning time. → Set clear learning goals. → Share what you learn through content. An hour a day of deliberate learning can be the antidote to career stagnation. 3. Step outside your comfort zone. Break through plateaus by tackling challenges that push your limits. When things feel uncomfortable, you’re on the right path. → Try new projects. → Pick up complementary skills. → Start before you feel fully ready. Discomfort means you’re growing—keep pushing forward. 4. Let go of outdated thinking. Don't cling to old methods just because they once worked. Continuously update your mental models to stay agile in a changing world. → Question established “best practices.” → Adapt when new information emerges. → Be curious about new tech. What worked yesterday won’t always work tomorrow. 5. Turn knowledge into impact. Experience > knowledge. → Apply your knowledge by building or creating → Work on side projects to learn → Teach others what you know. Don't just consume. Create. Remember: Never stop learning, growing, and stretching yourself. What are you currently learning? Let me know what you're working on in the comments. ♻️ Repost to help someone grow their career. ➕ Follow me, Owain Lewis to stay in touch.

To scale a company, you need a culture of continuous improvement. That means telling your team 3 things: 1) imperfections are OK, as long as you're working to raise the bar; 2) instead of stressing about what you can't control, focus on the important things you can; and 3) we're all in the same boat and will work together to solve problems. To unpack that: 1) Imperfections are OK, as long as you're working to raise the bar. A high-growth startup feels like it's constantly growing out of its shoes, but that's part of the process. If everything is running perfectly and feels like it's going smoothly – we're probably not going fast enough. It's not about perfection, it's about continuous improvement. 2) Instead of stressing about what you can't control, focus on the important things you can. I often ask myself and my team: how many things can we worry about at a time? Focus on the most pressing things, work on solving them and then move to the next challenges. It's all about step-by-step improvement. 3) We're all in the same boat. I think this is one of most important things to remind ourselves of. Everyone is working towards the same goal, which means we're all working on solving these problems and improving together – and that's exciting. You can't grow a company without acknowledging the chaos that comes with it and letting your team know that's okay. If your true North Star is continuous improvement, the growth will come naturally.

“We used to in every class have a Discord. It used to be like a lot of people just asking questions about maybe like, a lab or a homework... I guess everyone’s just Chat-GPT now. Like the new classes that I have now, we still have the Discord, but nobody really talks because most or all the questions are answered by ChatGPT.” —P16, undergraduate computing student If you’ve moderated a class Discord, you’ve probably felt this shift: a once-busy channel that used to hum with “anyone stuck on Q3?” goes quiet. Not because students stopped needing help, but because they started getting it elsewhere. A new study by Hou et al puts language to what many of us have sensed. Based on 17 interviews across seven R1 universities, students described a social rerouting of help-seeking: 13 of 17 said peer requests are now mediated by GenAI (often “ask GPT”), and students noticed community spaces like Discord slowing down. However, when AI becomes the first responder, the “hidden curriculum” stops circulating. Fewer quick questions means fewer micro-mentorships, fewer perspective-shifts, less socially shared regulation — all the good stuff that builds belonging and lifts performance over time. Students save minutes, but communities lose momentum. So what can educators do about this? - Design “peer-first, AI-fast” protocols. Peer interactions build camaraderie and a sense of belonging. Educators need to design experiences that build more peer interactions and support inside classrooms, to compensate for GenAI caused declines.   - Protect mentorship routes. Research also showed that younger students are reaching out less often to senior mentors, missing out on invisible learning that comes from understanding unwritten rules and cultural norms. Educators might need to formalize “office-hours relays” (senior → junior → cohort) so guidance doesn’t vanish.  - Create informal interaction opportunities. Informal opportunities help students build relationships beyond their immediate circle, and provide entry points into additional learning communities. Have you seen AI change the quality of collaboration in your learning or work spaces? How can we preserve the “hidden curriculum” when AI takes over first-line help?  #GenAI #Education #PeerInteraction #HiddenCurriculum

𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝗽𝗲𝗻𝗱 𝟰𝟬𝟬 𝗵𝗼𝘂𝗿𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗪𝗦 𝗮𝗻𝗱 𝘀𝘁𝗶𝗹𝗹 𝗴𝗲𝘁 𝗶𝗴𝗻𝗼𝗿𝗲𝗱. Here’s why you’re not getting hired, and how to flip the game. Most people treat the cloud like school: 📚 Study. 📝 Test. 🎓 Cert. Then… silence. No job. No calls. No shot. Why? Because you’ve built 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲, not 𝘃𝗮𝗹𝘂𝗲. Here’s what the people who go from “𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴” to $80, $90 or even $100K+ offers actually do (that no course will teach you): 𝟭. 𝗧𝗵𝗲𝘆 𝗕𝘂𝗶𝗹𝗱 “𝗣𝗿𝗼𝗼𝗳 𝗔𝘀𝘀𝗲𝘁𝘀,” 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Projects are good. But 𝘗𝘳𝘰𝘰𝘧 ��𝘴𝘴𝘦𝘵𝘴 are better. This means: ✅ GitHub repo + architecture diagram ✅ Loom walkthrough: "Here’s how I built it & why" ✅ LinkedIn post: “Business impact of my cloud solution” ✅ Resume bullet: “Reduced X by Y using Z” They don’t just 𝘣𝘶𝘪𝘭𝘥 stuff, they 𝘱𝘢𝘤𝘬𝘢𝘨𝘦 it like a portfolio pitch deck. 𝟮. 𝗧𝗵𝗲𝘆 𝗦𝗼𝗹𝘃𝗲 𝗜𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 Most beginners build what’s 𝘰𝘣𝘷𝘪𝘰𝘶𝘴 (launch an EC2, host a static site). The ones that want the offer, build what’s 𝘶𝘯𝘥𝘦𝘳𝘷𝘢𝘭𝘶𝘦𝘥: “Automated IAM cleanup across dev/test accounts” “Created centralized logging using ELK & S3 lifecycle policies” “Built a budget alerting system for sandbox projects” These sound advanced, but they’re not. They just 𝘀𝗼𝗹𝘃𝗲 𝗿𝗲𝗮𝗹 𝗽𝗮𝗶𝗻𝘀 companies actually deal with. 𝟯. 𝗧𝗵𝗲𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 They don’t just say, 👉🏾 “I set up a VPC.” They say, 👉🏾 “I designed a 3-tier VPC for a fintech app that needed PCI-DSS compliance, public ELB, private app + DB tiers, NAT gateway for secure outbound traffic.” Even if it’s all mock, it 𝘵𝘦𝘭𝘭𝘴 𝘱𝘦𝘰𝘱𝘭𝘦: 🎯 “I think like an engineer.” 🎯 “I understand context.” 🎯 “I can walk into your problem and build something that makes sense.” 𝟰. 𝗧𝗵𝗲𝘆 𝗥𝗲𝘃𝗲𝗿𝘀𝗲-𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗝𝗼𝗯 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻𝘀 Every cloud job is a cheat sheet. Instead of guessing what to build, they: * Pull 10 job posts * Circle every tool/problem mentioned * Build mini-projects around those * Post their journey like a series: “One week, one use case” 👉🏾 By week 5, they’ve built a portfolio targeted to actual market demand. 𝟱. 𝗧𝗵𝗲𝘆 𝗔𝗰𝘁 𝗟𝗶𝗸𝗲 𝗧𝗵𝗲𝘆 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗕𝗲𝗹𝗼𝗻𝗴 This is subtle but massive: They don’t “hope to break in.” They speak, share, and build like they’re already in. Their content doesn’t say: “I’m learning cloud.” It says: “Here’s how I think about cloud architecture.” That energy gets noticed. That mindset 𝗽𝘂𝗹𝗹𝘀 𝗗𝗠𝘀. That shift = leverage to show you can solve THIER problem. Want to Actually Get Hired? Stop going after all certs. Start proving capability. Start showing how you solve problems. 💬 Drop “𝗣𝗥𝗢𝗢𝗙” if you want the full list of value-packed, business-focused projects that actually convert to interviews. I'll send you access to them Let’s make the work, 𝘸𝘰𝘳𝘬 for you.

Mindfulness and Decision-Making: Schedule Smart Decisions Productivity isn’t just about what you do—it’s about when you decide. Research shows that our mental energy isn’t limitless. If you’ve ever felt “decision fatigue” by the afternoon, you know how tough it can be to make clear choices after a long day. That’s why mindful leaders schedule important decisions at times when they’re most alert, focused, and calm. ✨ Here’s a simple way to start: 1.Notice when your energy is highest during the day (for some it’s early morning, for others mid-morning). 2.Reserve that time for decisions that require creativity, strategy, or emotional clarity. 3.Leave the lower-energy parts of the day for admin, routine tasks, or light reviews. This small shift can reduce stress, save energy, and help you make decisions with confidence—rather than out of exhaustion. 🗓️ Try it tomorrow: Block 30 minutes during your peak energy window for an important decision you’ve been putting off. #Mindfulness #DecisionMaking #Productivity #LeadershipDevelopment #Clarity #Focus

Throughout my 30+ years journey leading textile and manufacturing operations, I've witnessed firsthand how the Kaizen philosophy has revolutionised organisational culture. It's not about grand, sweeping changes – it's about the compound effect of small, continuous improvements. The true essence of Kaizen lies in its simplicity and accessibility: • It transforms workplace culture from "That's not my job" to "How can I help?" • Empowers every employee to become a problem solver • Creates a sustainable framework for innovation • Builds resilience through continuous adaptation The most powerful transformations often begin with the smallest steps.  When every team member contributes daily improvements, the collective impact becomes extraordinary. Based on decades of leadership experience, here are three proven pillars of successful Kaizen implementation: 1. Leadership Through Gemba Walks Leaders must be visible on the shop floor. When we observe and engage directly with processes and people, real transformation begins. 2. Front-line Empowerment Your operators know the processes best. Give them the tools and authority to solve problems and watch innovation flourish. 3. Celebrate Progress Recognition drives repetition. Make it a habit to acknowledge improvements, no matter how small. Remember: Excellence is not a destination; it's a continuous journey of improvement. #leadership #team #peoplemangement #culture #kaizen #organizationculture #LeadwithRajeev