Productivity

Most everyone has heard the advice to take a walk, ideally in nature, whenever we need to reset our mind. Dr. Marc Berman, a professor of psychology and neuroscience at the University of Chicago, has actually run experiments on this approach and finds that, provided you do it without looking at your phone and you pay attention to your surroundings, the specific fractal patterns present in nature—fractals are naturally occurring repeating patterns at different size scales—can potently restore your ability to focus. During the podcast we also discuss things you can do for your indoor environment to make it more conducive to focus and relaxation. If you’re interested in increasing your focus with behavioral tools, the episode covers a variety of science-supported tools known to be effective for this—yes, “take a walk in nature,” but many other tools as well.

🔎 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗱𝗲 𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹 AMD 𝗰𝗵𝗶𝗽! 😲 Here's a bit of a Ryzen processor made on TSMC's 7-nanometer node. You can see the web of interconnects, the metal wires that connect the transistors (that bottom layer) on a chip to harness their computing power. The image was taken with a new 𝗽𝘁𝘆𝗰𝗵𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗫-𝗿𝗮𝘆 𝗹𝗮𝗺𝗶𝗻𝗼𝗴𝗿𝗮𝗽𝗵𝘆 (𝗣𝘆𝗫𝗟) technique out of the PSI Paul Scherrer Institut, University of Southern California and ETH Zürich. The technique currently has 4 nanometer resolution and the scientists have a path to get to 1 nm resolution. The cool thing about this technology is its non-destructive imaging power to help find defects in chips. Today’s chips are so complicated that electrical tests alone can no longer pinpoint where a defect is: chipmakers use a mix of optical imaging and other methods to zero in on potential problem areas. They then image such areas with a slow but very high-resolution scanning electron microscope. Finally they might take a slice of a chip for further imaging with a transmission electron microscope (TEM). When they find the flaw, they can then go back and correct their design. But with PyXL, they have another tool to pinpoint defects without destroying the chip. ✨

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.

The silent productivity killer you've never heard of... Attention Residue (and 3 strategies to fight back): The concept of "attention residue" was first identified by University of Washington business professor Dr. Sophie Leroy in 2009. The idea is quite simple: There is a cognitive cost to shifting your attention from one task to another. When our attention is shifted, there is a "residue" that remains in the brain and impairs our cognitive performance on the new task. Put differently, you may think your attention has fully shifted to the next task, but your brain has a lag—it thinks otherwise! It's relatively easy to find examples of this effect in your own life: • You get on a call but are still thinking about the prior call. • An email pops up during meeting and derails your focus. • You check your phone during a lecture and can't refocus afterwards. There are two key points worth noting here: 1. The research indicates it doesn't seem to matter whether the task switch is "macro" (i.e. moving from one major task to the next) or "micro" (i.e. pausing one major task for a quick check on some minor task). 2. The challenge is even more pronounced in a remote/hybrid world, where we're free to roam the internet, have our chat apps open, and check our phones all while appearing to be focused in a Zoom meeting. With apologies to any self-proclaimed proficient multitaskers, the research is very clear: Every single time you call upon your brain to move away from one task and toward another, you are hurting its performance—your work quality and efficiency suffer. Author Cal Newport puts it well: "If, like most, you rarely go more than 10–15 minutes without a just check, you have effectively put yourself in a persistent state of self-imposed cognitive handicap." Here are three strategies to manage attention residue and fight back: 1. Focus Work Blocks: Block time on your calendar for sprints of focused energy. Set a timer for a 45-90 minute window, close everything except the task at hand, and focus on one thing. It works wonders. 2. Take a Breather: Whenever possible, create open windows of 5-15 minutes between higher value tasks. Schedule 25-minute calls. Block those windows on your calendar. During them, take a walk or close your eyes and breathe. 3. Batch Processing: You still have to reply to messages and emails. Pick a few windows during the day when you will deeply focus on the task of processing and replying to these. Your response quality will go up from this batching, and they won't bleed into the rest of your day. Attention residue is a silent killer of your work quality and efficiency. Understanding it—and taking the steps to fight back—will have an immediate positive impact on your work and life. If you enjoyed this or learned something, share it with others and follow me Sahil Bloom for more in future! The beautiful visualization is by Roberto Ferraro.

Right now, every CEO is wondering the same thing: “How can artificial intelligence help maximize our impact?”   Delivering on the promise of AI isn’t just good business, it has the potential to help us address some of society’s most pressing challenges. So today, I wanted to offer a closer look at how AI is helping us discover new medicines at Novartis.   The process of identifying a new drug, running patient clinical trials, and bringing it to market takes over a decade. Each new medicine costs on average $2 billion to develop, and we know nearly 9 in 10 of the treatments we work on will fail before they ever reach patients.   A major early step in that process is identifying individual targets in the body that we want to design a drug for. Once we identify that target, which most commonly is a protein, we look for molecules that might address the target’s underlying issue – ultimately those molecule structures form the basis for every successful treatment.   Unlocking the right protein and molecular structures is complex stuff – each step often takes years to get right and our scientists consider billions of potential chemical structures that might lead to effective and safe drug candidates.   AI offers us the chance to accelerate that process. Working with partners at Isomorphic Labs – including members of the Google DeepMind team that were awarded the Nobel Prize this year – we’re now able to do things like model how a protein folds and interacts with the molecules we design. AI models also make it possible for us to analyze different chemical structures simultaneously. It has the potential to add up to significant time savings for our drug development scientists and their work to predict what molecules might treat specific diseases better and faster.   We’re just at the beginning of what this technology can do. As we incorporate AI throughout Novartis’ work, I’m excited to see all the ways it helps us unlock the mysteries of human biology, so we can deliver better medicines that improve and extend patients’ lives.

I struggled with work/life balance throughout my career. This is because the world has set a clever, two-part trap for us. I will explain the trap and how to escape it. Part One – Our own goals and ambitions. I wanted to be successful, to get more pay, and to be a part of bigger decisions. If you follow me here, I bet you are the same. You want to “be the best” and have a great career. Part Two – Corporate pressure. Companies have a simple goal of making profits for shareholders. This is most easily done by getting more work from the same people. The Trap: The two parts converge to destroy work/life balance because our healthy desire to do good work, earn a living, and find meaning is easily manipulated by corporate systems designed to maximize profits. Here is how they do it: 1) Most companies give bigger raises to “better” performers. What is better? Usually, doing more work. Sometimes you can be “better” by being smarter or more efficient, but over time even the best of us usually work harder 2) Competition. Since raises and promotions are limited in number, there will always be someone else willing to put in very long hours to come out ahead of you. Some of you will recognize this as “the prisoner’s dilemma” – if only one person works harder, they will get a lot of advantages for only a little extra work. But, when we all strive to be first it becomes a maximum effort race with no winners. Ways to Escape the Trap: 1) Set limits. Recognize the trap and decide what you will and will not give to your work. This may mean accepting some career tradeoffs, but unless you set the limits your body will do it for you over time. It is better to make the choices yourself. 2) Seek work only you can do. We are all gifted at some things, and you get two benefits from focusing on your gifts. First, you can stay ahead of others with less effort. Second, it is more fun to do things that come easily. 3) Choose companies and bosses wisely. Some leaders push you into the trap, some leaders try to keep you out of it. Seek those that keep you out. 4) Work for yourself. If you can be your own boss you can escape the corporate side of profit maximization, or at least have it under your control. 5) Redefine success. There is nothing wrong with wanting pay, promotions, influence, etc. But if the cost gets too high, remember that plenty of people are happy without corporate success. My own path was to climb the ladder, make the money, and then step off. I sacrificed many good years to work and high stress in order to get a set of years without it. A good trade? Time will tell. Readers, what are some other ways to escape the trap?

Not all AI agents are created equal — and the framework you choose shapes your system's intelligence, adaptability, and real-world value. As we transition from monolithic LLM apps to 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, developers and organizations are seeking frameworks that can support 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, and 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘁𝗮𝘀𝗸 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. I created this 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 to help you navigate the rapidly growing ecosystem. It outlines the 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗶𝗱𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 of the leading platforms — including LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and more. Here’s what stood out during my analysis: ↳ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 is emerging as the go-to for 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — perfect for self-improving, traceable AI pipelines.  ↳ 𝗖𝗿𝗲𝘄𝗔𝗜 stands out for 𝘁𝗲𝗮𝗺-𝗯𝗮𝘀𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, useful in project management, healthcare, and creative strategy.  ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 quietly brings 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 to the agent conversation — a key need for regulated industries.    ↳ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 simplifies the build-out of 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀 through robust context handling and custom roles.  ↳ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 is refreshingly light — ideal for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗺𝗮𝗹𝗹-𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀.  ↳ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 continues to shine as a sandbox for 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and open experimentation. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗵𝘆𝗽𝗲 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀: - Are you building enterprise software with strict compliance needs?   - Do you need agents to collaborate like cross-functional teams?   - Are you optimizing for memory, modularity, or speed to market? This visual guide is built to help you and your team 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. Curious what you're building — and which framework you're betting on?

My biggest takeaways from Ethan Smith on how to win at AEO (i.e. get ChatGPT to recommend your product): 1. Being mentioned most often beats ranking first. In Google, the #1 blue link wins. In ChatGPT, the answer summarizes multiple sources—so appearing in five citations beats ranking #1 in one. Ethan’s strategy: get mentioned on Reddit, YouTube, blogs, and affiliates. Volume of mentions matters more than any single placement. 2. LLM traffic converts 6x better than Google search traffic. Webflow saw this dramatic difference because users who come through AI assistants have built up much more intent through conversation and follow-up questions, making them highly qualified leads. 3. Early-stage startups can win at AEO immediately, unlike with SEO. Traditional SEO requires years of domain authority. But a brand-new Y Combinator company mentioned in a Reddit thread today can show up in ChatGPT tomorrow. The playing field is finally level. 4. The long tail of AEO is 4x bigger than SEO. People ask ChatGPT questions with 25 or more words (vs. 6 in Google). Ethan found gold in queries like “Which meeting transcription tool integrates with Looker via Zapier to BigQuery?”—questions that never existed in search but are perfect for AI. Own these micro-niches. 5. Reddit is proving to be the kingmaker for AI visibility. ChatGPT trusts Reddit because the community polices spam better than any algorithm. Ethan’s exact playbook: make one real account, say who you are and where you work, give genuinely helpful answers. Five good comments can transform your visibility. No automation, no fake accounts—just be helpful. 6. YouTube videos for “boring” B2B terms are a gold mine for AEO. Nobody makes videos about “AI-powered payment processing APIs”—which is exactly why you should. While everyone fights over “best CRM software,” the high-value, zero-competition long tail is wide open in video. 7. Your help center is now a growth channel. All those “Does your product do X?” questions flooding ChatGPT can be answered by help-center pages. Move them from subdomain to subdirectory, cross-link aggressively, and cover every feature question. Ethan calls this the most underutilized opportunity in AEO. 8. January 2025 was the inflection point in AEO growth. That’s when ChatGPT made answers more clickable (maps, shopping cards, citations) and adoption exploded. Webflow went from near zero to 8% of signups from AI. This channel is accelerating faster than any Ethan’s seen in 18 years. 9. The AEO playbook: (1) Find questions from competitor paid search data, (2) set up answer tracking, (3) see who’s showing up as citations, (4) create landing pages answering all follow-up questions, (5) get mentioned offsite via Reddit/YouTube/affiliates, (6) run controlled experiments, (7) build a dedicated team. This exact process is driving real results at scale.

Amazon is a ‘search’ platform. 50-70% of shoppers across categories are searchers, not browsers. Unlike ‘browse’ heavy platforms like Nykaa, Myntra, Cred and others, journeys start and end with a search or two. Being visible on searches is the game. The problem is that all top listings are advertisements you need to bid for. This performance marketing is addictive because one, it gives quick returns and two, reducing spends has direct impact on revenue. But it is expensive if not done efficiently or if done in vanity. Thousands of brands have tried to gain traction through AMS only and ended up in the burial ground. It’s a death spiral. The only way one can survive selling on Amazon is if a significant portion of sales comes organically. And for that one needs to rank higher organically. Amazon uses the A10 algorithm to rank products according to relevance to search. It’s an almost black box but some factors it seems to assign weights to are: 1. Search relevance: it checks keywords in the front-end, back-end, descriptions and rest of listing including richness of A+ content. 2. Consistency of sales velocity: OOS affects it badly. Fluctuations affect it badly. Grow steady and fast, preferably steady. 3. External signals: ratings, reviews and external traffic’s weight has been increased in A10 compared to A9. Not much else matters if your ratings are poor. Ratings affect the factors that follow next. A double whammy! 4. Click through rates: What % of people who saw your listing clicked on it. A function of first listing card and delivery time among others. 5. Conversion rates: What % of people who saw your listing went on to buy. 6. Seller Authority: your karma matters. Keep on doing the right things and the system rewards. Fall in the trap of a quick buck and you back a couple of steps.

Wikipedia traffic is collapsing — and it’s not just because of AI. Wikipedia just reported an 8% drop in human visits in just a few months. The reason? AI systems — the same ones trained on Wikipedia — are now answering questions instead of sending users there. The free encyclopedia is being replaced by the knowledge it taught. That irony stopped me cold. I’ve always seen Wikipedia as the internet’s moral compass — messy, human, collaborative. When I was learning about anything new, I didn’t go for perfection. I went for context. Now I rarely visit it. AI gives me the answer instantly — but never the understanding that came from scrolling, cross-checking, exploring footnotes. Somewhere along the way, convenience quietly replaced curiosity. Here’s what’s really going on beneath the numbers: → AI is not just summarizing information — it’s absorbing the audience that once sustained the sources. → When answers appear directly on search pages, the human loop of reading, editing, and donating breaks. → And as fewer humans visit, fewer volunteers contribute — shrinking the very ecosystem AI depends on. It’s the classic paradox of automation: AI is killing the teachers it learned from. If knowledge itself is becoming automated, we need to rebuild the habit of participation. Here’s what I believe that looks like: ✅ Credit and link back to the human sources behind AI summaries. ✅ Support open, editable knowledge platforms — not just polished AI outputs. ✅ Remember that understanding comes from reading, not just receiving. Because if we stop feeding the commons of human knowledge, We won’t just lose Wikipedia — We’ll lose the curiosity that made the internet worth exploring in the first place. #AI #Wikipedia #KnowledgeEconomy #AIEthics #Publishing #InformationFuture #DigitalCulture