Technology

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝗵𝗲𝗮𝗹𝘁𝗵 𝘁𝗲𝗰𝗵: 𝗧𝗵𝗲 𝗺𝗼𝗿𝗲 𝘄𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗮𝗻𝘅𝗶𝗼𝘂𝘀 𝘄𝗲 𝗯𝗲𝗰𝗼𝗺𝗲. We track our bodies 24/7. Count every calorie. Measure sleep, HRV, glucose, stress. From Apple Watch. To Oura Ring. To the latest “temple” device. Somewhere along the way, awareness turned into obsession. Here’s the paradox no one talks about: We have the best health-tracking tools in history, and some of the worst health outcomes. Something doesn’t add up. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗼𝘄𝘀 𝗦𝗹𝗲𝗲𝗽 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗰𝗮𝗻 𝘄𝗼𝗿𝘀𝗲𝗻 𝘀𝗹𝗲𝗲𝗽 Studies on orthosomnia (an obsession with “perfect” sleep metrics) show that people who fixate on sleep scores experience more sleep anxiety, lighter sleep, and poorer recovery—even when objective sleep doesn’t improve. Trying to optimize sleep can literally break it. 𝗛𝗥𝗩 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘀𝘁𝗿𝗲𝘀𝘀 𝗳𝗼𝗿 𝗺𝗮𝗻𝘆 𝘂𝘀𝗲𝗿𝘀 HRV is a useful trend marker—but daily fluctuations are normal. Research shows that constant HRV checking can heighten health anxiety and perceived stress, especially when users don’t understand variability or context. Ironically, stressing about HRV often lowers HRV. 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Behavioral science research consistently finds that excessive self-monitoring leads to hypervigilance, loss of bodily trust, and decision fatigue. When every sensation becomes a data point, people stop listening to internal cues and start deferring to dashboards. In short: 𝗢𝘃𝗲𝗿-𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗮𝗻𝘅𝗶𝗲𝘁𝘆. So what actually creates health? The same fundamentals that worked 5,000 years ago: • Deep, peaceful sleep • Regular sunlight • Real, nourishing food • Daily movement • Time with people you love These don’t need algorithms. They need presence. Use wearables if they serve you—I do, occasionally. But don’t let them become your master. Your life isn’t an algorithm waiting to be optimized. It’s a system meant to be felt, explored, and course-corrected. The best health coach you’ll ever have is already inside you. Trust it.

Today in Cell, we published new research showing how AI can help accelerate cancer discovery. With GigaTIME, we can now simulate spatial proteomics from routine pathology slides, enabling population-scale analysis of tumor microenvironments across dozens of cancer types and hundreds of subtypes.   Developed in partnership with Providence and the University of Washington, our hope is that this work helps scientists move faster from data to insight, revealing new links between genetic mutations, immune activity, and clinical outcomes, and ultimately improving health for people everywhere. https://lnkd.in/dSpPdtzz

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

🗞️ A must-have for anyone teaching Russian disinformation tactics. A comprehensive yet highly pedagogical and illustrated catalogue of tactics with concrete examples. 👏🏼Well done @center for countering disinformation with the support of The European Union Advisory Mission Ukraine (#EUAM Ukraine) 🇪🇺 1️⃣ The first part is dedicated to the Mechanisms of destructive information influence: • Bots 🤖 • Fake accounts 🤳🏻 • Anonymous authority 👁️ • Appeal to authority 🔨 • Deepfakes 👾 • Potemkin villages 🤡 • Duplicating websites or accounts 👨🏻💻 • Framing 🖼️ • Information overload 🌧️ • Agenda-setting 📆 • Demonisation • Polarisation 🤯 • Confirmation bias 🧠 • Primacy effect 🪢 • Deceptive sources 🎭 • Information alibi 🥸 2️⃣ The second part offers an overview of the Tactics of destructive information influence. Particularly useful to identifies the perverse rhetorical tricks at play and counter them with the right arguments: • Clickbaiting • Rating • Information sandwich • Lost in translation • Presence effects • Contextomy • Gish gallop • Whataboutism • Conspiracy theories • Talking away • Mundanisation • Doublespeak • Sleeper effect • “Check it if you can” • False analogy • Trolling • False dilemma • Using jokes or memes • Stereotyping 3️⃣ The last part describes the various soft power tools weaponized to leverage influence : Soft power tools: Russia’s influence through… • films 🎦 • e-sports 🎮 • literature 📕 • music 🎶 • sports ⚽️ • churches ⛪️ • cultural centre networks 🤝🏻 • educational programmes and grants 🎓 • historical revisionism 🖊️ • loyal political structures🏰 👐🏻Many thanks to the authors for a reference document which deserves to be widely shared As someone who srudied humanities, I always longed for the ancient “class of rhetorics” which was, until the late 19th century, the penultimate year of secondary education in France before philosophy: students learned the full art of persuasion—finding ideas, structuring them, refining style, memorizing, and delivering speeches—through constant practice and study of classical models. The purpose was to train them in the art of eloquence—to speak and write clearly, elegantly, and persuasively. And to prepare future orators -lawyers, priests, politicians- as well as any educated citizen. Were this classical knowledge more widely shared today, we might be better equipped to resist the tactics outlined in part 2️⃣ as we would more spontaneously recognize the persuasion strategies used against us -even if they come in alluring video forms these days! - and be able to counter them with the tools of logic and structured argument.

By 2030, these 11 abilities will decide who gets hired Most don’t show up on resumes yet. The World Economic Forum just revealed the top skills for 2030 in the Future of Jobs Report 2025. And it’s a wake-up call. Today's celebrated tech skills? AI will do those better by 2026. Those certifications? Outdated in 18 months. But here's the good news: The skills that matter most in 2030? Technology can't replace them. Start mastering these skills to stay relevant and be recognized: 1. AI and Big Data 🤖 ❌ Passively watch AI replace jobs ✅ Make AI your competitive edge → Use AI to automate weekly reports → Build self-updating dashboards and summaries 2. Analytical Thinking 🧠 ❌ Drown in opinions and noise ✅ Let data drive key decisions → Identify root causes before reacting → Monitor metrics that reveal blind spots 3. Resilience, Flexibility and Agility 🐆 ❌ Break down under shifting priorities ✅ Adapt fast and lead through change → Stay steady during messy execution → Pause, breathe, ask: “What’s the next best move now?” 4. Motivation and Self-Awareness 👤 ❌ Burn out chasing urgency ✅ Work in sync with your energy → Track your energy every 3 hours for a week → Schedule focus work when your mind feels sharp 5. Curiosity and Lifelong Learning 🔍 ❌ Stick to your job description ✅ Learn a complementary skill to your role → If you're in marketing, study basic product design → If you're in finance, explore storytelling with data 6. Leadership and Social Influence 🌟 ❌ Rely on your title for respect ✅ Build trust by how you think, speak and act → Explain why you made a tough call, not just what you decided → Share a client insight that helped your team level up 7. Technological Literacy 💻 ❌ Run to the IT helpdesk for every issue ✅ Build and adapt your own stack → Automate one repetitive workflow today using AI → Use familiar tools more efficiently (Excel, Slack) 8. Systems Thinking 🔧 ❌ React to broken processes ✅ Design workflows that scale → Improve one repeated but inefficient process this week → Ask: “Can this run without me?” 9. Empathy and Active Listening 🎧 ❌ Talk to be heard ✅ Listen to support, inspire and lead → Listen without needing to speak more in 1:1s → Decode what’s really being said 10. Creative Thinking 🎨 ❌ Wait for inspiration ✅ Build innovation into routine → Ask: “What’s another way to solve this?” → Try a small change to test a new idea 11. Talent Management 👥 ❌ Try to do it all ✅ Delegate and develop future leaders → List 3 tasks to delegate now → Improve hiring processes to onboard the right talent 💡 It’s not about doing more. It’s about evolving how you think, lead, and grow. Because the future expects you to. Which one are you focusing on this month? -- ♻ Share this with someone you’d want on your 2030 team. ➕ Follow me (Meera Remani) for future-ready leadership strategies. 🔔 My best insights for transforming your leadership career? Join my exclusive email list. Link below.

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

#Batteries are starting to dominate the evening peak in California's grid, charging up with daytime solar then discharging as solar ramps down. On 5th April they set another new record for share of supply, peaking at over 34% at 7pm. This represents a rapid progression - two years ago the record was just 13%. And they remained the largest source of supply on the grid from 6:35pm until 9:40pm. As more and more battery storage enters the mix, batteries will continue to play an increasing role in the state's grid, and continue to break more records. They are flexible and extremely quick to respond. By charging in the middle of the day they are soaking up excess solar and are then putting this to good use later, reducing the need for gas and imports in the nighttime hours. From just 0.5 GW in 2018, by late 2024 California already had over 13 GW of battery storage capacity, with more on the way. While that may sound like a lot, there is still some way to go with the California Energy Commission estimating the state will need around 52 GW of battery storage to meet it's 2045 target of getting all its power from carbon-free sources. Batteries will play an important role in the decarbonised grid of the future. As prices continue to fall we will see more and more batteries deployed, and are certainly seeing this happen in Australia - especially Western Australia. We are just on the cusp of much more widespread adoption. Onwards and upwards! #energy #sustainability #renewables #energytransition

You can't patch your way to the future. Picture this: a wide-open road stretching ahead, the left lane marked by familiar potholes, outdated signs, and traffic jams—let’s call that your legacy systems. On the right, a shiny, newly paved lane labeled Industry 4.0, buzzing with autonomous cars, AI-driven signs, and smart sensors optimizing traffic flow. And then, right in the middle, a single Band-Aid stretching feebly across a gaping crack labeled "digital divide," proudly announcing: "Another Dashboard!" Sound familiar? Too often, companies attempt digital transformation by tossing yet another fancy dashboard at deeply embedded, systemic problems. Dashboards are like Band-Aids—they might momentarily cover the crack, providing short-term comfort, but they never truly fix the road beneath. Here's why another dashboard won't save your company from the digital divide: 𝟏. 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫𝐥𝐨𝐚𝐝 ≠ 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 Dashboards multiply data visibility, but without meaningful integration and actionable insights, they're just colorful confusion. It's like adding mirrors to your car to avoid potholes—you might see them better, but they're still there. 𝟐. 𝐒𝐮𝐫𝐟𝐚𝐜𝐞-𝐥𝐞𝐯𝐞𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐅𝐚𝐢𝐥 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 A Band-Aid might be great for a scraped knee, but it's laughable when bridging a canyon-sized technology gap. True digital transformation requires fundamental changes to business processes, culture, and underlying technology. 𝟑. 𝐘𝐨𝐮'𝐫𝐞 𝐃𝐞𝐥𝐚𝐲𝐢𝐧𝐠 𝐭𝐡𝐞 𝐈𝐧𝐞𝐯𝐢𝐭𝐚𝐛𝐥𝐞 Quick fixes feel good now, but eventually, you'll still need to fix the underlying problems. Industry 4.0 isn't about simply digitizing old processes—it's about completely rethinking how your business operates. So, what's the real fix? Instead of Band-Aids, you need road construction. Invest in infrastructure—robust data architectures, AI-driven analytics, interconnected systems, and (most importantly) organizational alignment around digital goals. This isn't about just observing your problems; it's about solving them. Dashboards have their place—but not as patches. Use them as windows into new opportunities, supported by sturdy, future-proof foundations. 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e7wyDU_B ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!