Technology

Hot take: the #legalengineer is now the most critical role in the in-house legal department. Not the GC. Not the deputy. Not the head of legal ops. The person who sits at the intersection of legal process expertise, technology fluency, and change management and who can re-engineer how legal work gets done as AI reshapes what's possible is what separates the teams that will come out of this period ahead from the ones that will have a lot of expensive technology and not much to show for it. In-house legal is redesigning itself right now. What goes to outside counsel? What does AI handle? How do we staff? You can't answer those questions or execute on the answers without someone who can architect the new model. I've been in this space for over two decades. This is the role I'd prioritize above almost anything else right now. https://lnkd.in/gCy6tQr5

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.

Software development is quietly undergoing its biggest shift in decades. Not because of new frameworks. Not because of faster cloud. But because agents are entering the SDLC. Traditional development follows a slow, sequential loop: requirements → design → coding → testing → reviews → deployment → monitoring → feedback. Each step depends on human handoffs, manual fixes, delayed feedback, and long iteration cycles—often stretching from weeks to months. Agentic coding changes this entirely. Instead of humans writing everything line-by-line, developers express intent. Agents understand requirements, implement features, generate tests and documentation, deploy changes, monitor production, and even propose fixes. The lifecycle compresses from weeks and months into hours or days. Here’s what actually changes: • Sequential handoffs become continuous agent-driven flows • Humans shift from coding to guiding and reviewing • Documentation is generated inline, not after delivery • Testing happens automatically alongside implementation • Incidents trigger agent-assisted remediation • Monitoring feeds directly back into learning loops • Iteration becomes constant, not episodic In the Agentic SDLC: You describe outcomes. Agents execute workflows. Humans validate critical decisions. Systems learn continuously. The result isn’t just faster delivery. It’s a fundamentally different operating model for engineering—where feedback is immediate, fixes are automated, and improvement never stops. This is how software teams move from manual development pipelines to self-improving delivery systems.

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.

👁 Imagine losing your sight for 10 years… and then, the very first thing you do is recognize the faces of your loved ones again. That’s what happened to Jamal Furani, 78, thanks to a breakthrough in medical innovation: a fully synthetic cornea implant. No donor tissue. No immune rejection. A device that integrates directly with the eye’s own tissue. 💡 The deeper insight: The true revolution here isn’t only technological. It’s structural. Today, corneal blindness affects millions worldwide, but most can’t be treated because there simply aren’t enough donor corneas. A synthetic cornea changes the equation. It turns a scarce resource (donations) into a potentially unlimited one (innovation). And here’s what few realize: this implant doesn’t just restore vision. It restores autonomy, dignity, and human connection. Those are the “side effects” that make technology truly transformative. 👉 My take: The future of medicine won’t just be about “healing.” It will be about reinventing our organs — sometimes with solutions even better than the originals. If you could enhance or replace one organ with technology, which would you choose first? #Healthcare #Innovation #Biotech #FutureOfMedicine

🗞️ 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.

Two strikingly similar headlines surfaced this past week that should make every leader pause: • “Companies Are Pouring Billions Into A.I. It Has Yet to Pay Off.” — New York Times • “Companies Are Pouring Billions Into AI. Here’s Why They’re Not Seeing Returns” — Forbes The NYT points to the human side: employees resist tools they don’t trust. Forbes focuses on the technical side: most AI still can’t understand the context of work. Both are true, and they’re related. When AI lacks context, employees lose trust. It can’t tell the latest doc from last year’s draft. It summarizes a customer conversation but drops the follow-ups buried in the thread. It pulls a response from Slack while ignoring the context in Google Drive. Employees realize it creates more work than it saves, and stop using it. Pilots stall, deployments fade, and projects slide into the “trough of disillusionment" as the NYT describes. Unfortunately, that's the reality for many organizations. At Glean, we work hard to make sure AI understands the enterprise context the way a human does. If a subject matter expert says something, I trust it more. If something’s old, I double-check it. That’s how people think, and it’s how AI should work too. Yet every enterprise has its own documentation culture and quirks, so sometimes we struggle at first. But we persist and co-develop with customers until the system reaches the quality they need. Then we take those learnings to make it work automatically for the next customer. We’ve seen this approach deliver measurable impact for customers: • Booking.com: Glean Agents give teams faster access to customer insights, cutting video production time by 75% and doubling monthly output. • Confluent: Glean’s AI-powered search saves 15,000+ hours/month, boosts support satisfaction by 13%, and cuts ticket investigation time by 10 minutes. • Fortune 100 telecom company: Glean surfaces instant knowledge during support calls, reducing call resolution time by 17 seconds across 800+ agents. • Leading global consultancy: Glean Agents automate RFP workflows, cutting consulting project proposals from 4 weeks to a few hours (97% faster). • Wealthsimple: Glean gives employees instant access to policies and knowledge, driving $1M+ in annual productivity gains. When AI understands the real context of work—across people, tools, and workflows— employees trust it and use it. Instead of falling into the trough of disillusionment, companies climb a slope toward productivity gains and real ROI.

AI just helped a couple get pregnant - after 19 years and 15 failed IVF cycles. The breakthrough came with an AI tool built by a team at Columbia University. It’s called STAR - the world’s first AI system trained to find sperm that embryologists can’t. The husband had azoospermia - a condition where no sperm is visible under the microscope. Dozens of attempts, surgeries, and even overseas experts had failed. But the team at Columbia didn’t give up. They spent 5 years building STAR (Sperm Track and Recovery). The system scans 8 million images per hour using a chip and computer vision, then gently isolates viable sperm missed by even the most experienced lab techs. And it worked. ▶︎ STAR found 44 sperm in a sample that had been manually searched for two full days. ▶︎ That one breakthrough led to a pregnancy that had felt impossible for nearly two decades. ▶︎ And it did so without chemicals, donor samples, or invasive extraction methods. For millions of couples dealing with infertility, this is a glimpse of what AI-assisted reproductive medicine could unlock. But more importantly - this shows us what AI in healthtech should be aiming for: Not just more data. Not just smarter models. But real clinical results that change lives. And as a healthtech investor, this is what I look for in AI-driven care: → A clear pain point → A targeted intervention → And a story no one can ignore What’s your take - could AI reshape fertility care the way it’s starting to reshape diagnostics and mental health? #entrepreneurship #healthtech #innovation