Productivity

🔎 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. ✨

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.

The ability to create clarity when there’s no shortage of chaos, opinions, and competing priorities is a rare skill. In any reasonably competent company, this skill alone will help take you quite far, fairly quickly. Concretely, this means creating clarity on the main problems, clarity on the right solutions, and clarity on the action plan & priorities. Very few people can do this well even though most people possess the intelligence necessary to do it. This is because most people in the workplace have been conditioned to add more information, sound more clever, satisfy more stakeholders, and feign more precision & certainty than is possible. Few understand that clarity in a chaotic situation can only emerge from subtraction, never from addition. Clarity comes from communicating what stands out as most important, why it is most important, how it will be achieved, and last but not the least, giving people a way of thinking about why it is okay, even great, that we aren’t doing All The Other Things.

I built an AI agent that handles my entire inbound system. (And I used to be against automation). Here's how I did it: I used two tools: --> Make: For automation workflows --> Relevance: For AI agents Here's what my AI agent handles: When someone fills our form, it- --> Analyzes their LinkedIn profile --> Reviews their website --> Checks if they match our criteria --> Makes a decision in seconds For qualified leads: --> Sends personalized pitch deck --> Books discovery calls --> Handles initial questions For non-qualified leads: --> Sends a thoughtful rejection --> Explains why we're not the right fit --> Keeps the door open for future The best part? My team and I can focus on what matters - strategy and client success - instead of spending hours on admin work. No more: -Manual lead checking -Back-and-forth emails -Calendar scheduling headaches -Just high-quality conversations with pre-qualified founders. Want to know the biggest lesson? Automation isn't about replacing the human touch. It's about creating more time for it.

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

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.

McKinsey & Company shows how Danone turns operations into a growth engine. A sharp interview by Pierre de la Boulaye and Søren Fritzen with Vikram Agarwal highlights a structural shift across the FMCG industry. For decades, operations were treated as a cost center. That paradigm is changing. Leading companies now position operations as a driver of growth and competitiveness. The transformation at Danone shows how AI, digital manufacturing and advanced supply chains are reshaping the sector. Several insights stand out. 1) AI turns factories predictive Operators increasingly monitor production lines via tablets instead of control rooms. AI systems detect potential equipment failures before they occur, for example overheating motors in packaging lines. Maintenance shifts from reactive repair to predictive intervention, improving uptime and efficiency. 2) Capacity planning becomes strategic Danone distinguishes three ways to build manufacturing capacity: • Release capacity from existing assets • Transform capacity by converting underperforming lines • Create capacity through new production investments Transforming existing lines enables growth with much lower capital intensity than building new factories. 3) AI reshapes supply chains Danone uses AI models to forecast ingredient costs and supply chain dynamics across global agricultural markets. Instead of analyzing thousands of variables, systems process millions of data points. For a company managing roughly €13.7B in COGS, forecasting accuracy becomes a competitive advantage. 4) Digital manufacturing at scale Danone’s Digital Manufacturing Acceleration program already covers 80+ factories, with 40 more joining soon, across 140+ production sites globally. The ambition goes beyond Industry 4.0 toward Industry 5.0, combining machines, AI and human expertise. 5) People remain central Danone employs 47,000+ people in operations, about half of its workforce. Through its Industry 5.0 Academy, the company has already trained around 20,000 employees in digital manufacturing capabilities. Why this matters The global FMCG industry generates over $4 trillion in annual sales and operates on tight margins. Even small improvements in forecasting, manufacturing efficiency or capacity utilization can translate into billions in value creation. As demand shifts toward health, high-protein and plant-based products, supply chains must become faster and more flexible. AI-driven operations are becoming a strategic advantage. The signal for FMCG leaders is clear: Competitive advantage is increasingly built beyond brands and marketing — in operations. #operations #manufacturing #ai #digitaltransformation #foodindustry #foodtech #retailtech #innovation #procurement #datadriven #danone #france #europe #startup #investors #marketing #sales #technology #logistics

Want to stay motivated every single day? Borrow a strategy from Harvard. Then borrow another from stand up comedy. Together, they’re a powerhouse for momentum, motivation, and mastery. Here’s how it works: Let’s start with Harvard. Researcher Teresa Amabile studied 12,000 daily work diaries across 8 companies. She wanted to know: What truly motivates people on a day to day basis? What she found changed how we understand drive. The #1 driver of daily motivation wasn’t: Money Praise Perks It was progress. The days people made progress on meaningful work were the days they felt the best. Progress isn’t a luxury. It’s a psychological necessity. So how do we make progress feel visible especially on days when it’s not? Use a “Progress Ritual.” → At the end of the day, pause. → Write down 3 small ways you moved forward. → That’s it. No fanfare. Just ritual. This works because we rarely notice our progress in real time. It gets buried under busyness, meetings, and mental noise. The act of looking back gives your brain the reward it needs to keep going. Momentum builds from meaning. Now let’s add some comedy. Young Jerry Seinfeld had one goal: write new material every day. To stay on track, he created a brilliant system. Each day he wrote, he put a big red X on his calendar. Soon, a chain of Xs formed. And here’s the key: Don’t break the chain. One red X becomes two. Two becomes ten. Ten becomes identity. Whether you’re writing, coding, or training Daily action + visual chain = long-term motivation. Summary: The Two-Part Motivation System From Harvard: Record 3 ways you made progress each day. From Seinfeld: Mark an X for each day you show up then don’t break the chain. Progress fuels purpose. Consistency fuels confidence. Apply both and you’ll stay on track especially on the tough days. Because when your days get better, your weeks get better. When your weeks get better, your months get better. When your months get better, your life gets better. It starts with one small win today.

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.

I create 247 pieces of content per month. Time spent? 7 hours. Most founders stare at blank screens for hours. They overthink, delete drafts, and convince themselves they're "not interesting enough." Here are 6 tips to generate endless content ideas: 1. Stop Waiting for Inspiration Content isn't about being on camera or feeling creative. It's about documenting what you're already doing, thinking, and learning every single day. 2. The Content GPS Framework Every week follows five buckets: Monday - mistakes I made, Tuesday - systems that work, Wednesday - client transformations, Thursday - contrarian truths, Friday - vision for the future. 3. The 30-in-30 Exercise Spend 30 minutes writing: 10 things that frustrate you, 10 lessons you've learned, 10 transformations you've witnessed. That's your content calendar. 4. Mine Your Past Self Last week at 2am in London, I asked myself one question: "What do I wish I knew 5 years ago?" Wrote 73 ideas in my journal without stopping. 5. Your Struggles Beat Their Quotes 20-somethings share motivational quotes. Real founders share scars.   Your authentic experience will always beat polished perfection. 6. Your Life IS Content Every decision you make, every system you build, every mistake you survive, it's all material waiting to be shared with people who need it. The difference between struggling and thriving with content? Systems beat motivation every time. I don't create content because I'm inspired. I create it because I have a framework that turns my real experiences into value for others. That's the power of building in public, transparency becomes your competitive advantage. Start documenting your journey today. Someone needs to hear exactly what you learned yesterday. __ Enjoy this? ♻️ Repost it to your network and follow Matt Gray for more. Want to learn how to create your content strategy? Join our community of 172,000+ subscribers today: https://lnkd.in/eTDRAcYa