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.

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 failed injection attempt is more than just a missed vein. It’s time lost. It’s patient discomfort. Sometimes, it’s fear that stays long after the needle is gone. I’ve seen it. You probably have too. We often underestimate how much trust is at stake in that single moment. Now, here’s what most people don’t know: near-infrared devices like VeinViewer are quietly changing this. They project invisible light into the skin. Blood absorbs it, tissue reflects it, and veins suddenly appear in high contrast — mapped right on the patient’s arm in real time. Studies show it makes a real difference: ✅ Higher first-stick success rates ✅ Less leakage, less pain ✅ Happier patients — especially children, people with small or hidden veins, and those who’ve lived through too many failed attempts But here’s the deeper insight: These devices don’t change outcomes equally for everyone. For easy cases, they don’t matter much. For difficult ones, they can be life-changing. That’s a lesson far beyond medicine: technology delivers its true value where the human struggle is greatest. To me, that’s the real story. Innovation isn’t about making the easy easier. It’s about transforming the moments where people suffer most. 💡 My take: The future of healthcare tech won’t be defined by speed or cost savings alone. It will be defined by whether we remember this — that behind every data point is a patient who just wants to be seen, heard, and spared unnecessary pain. 👉 Would you want your next IV placed with or without this tech? A brilliant invention by Christie Medical #Healthcare #Innovation #PatientExperience #MedTech #AI

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.

We’ve all heard about AI’s potential to boost productivity. But what truly matters to me is whether it’s making work better for the people who show up every day. At Cisco, our People Intelligence team, in collaboration with IT, has been exploring this very topic, and the findings are fascinating. Here are five key insights from our research that leaders should take seriously: 1. Leaders are key to adoption. At Cisco, employees are 2x more likely to use AI if their direct leader uses it. 2. Generic AI training doesn’t work. Role-specific, practical training accelerates AI use. 3. Confidence gaps exist among senior leaders. Directors at Cisco often feel less confident with AI than mid-level employees, underscoring the need for tailored support at all levels. 4. Employee autonomy fuels adoption. Hybrid work environments are powerful accelerators for AI adoption, while mandates can hinder it. Employees who voluntarily go to the office are more likely to use AI, while those who are required to work on-site have lower adoption. 5. AI use is linked to employee well-being, but the relationship is complex, with both benefits and trade-offs that require thoughtful navigation. This is just the beginning. Next, we’re looking at how AI is transforming the way teams operate. For now, one thing is clear, employees who use AI aren’t just more productive. They’re also more engaged, better aligned with company strategy, and empowered to focus on meaningful work. #AIAdoption #EmployeeExperience #FutureOfWork

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

Demystifying the Software Testing 1️⃣ 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: Unit Testing: Isolating individual code units to ensure they work as expected. Think of it as testing each brick before building a wall. Integration Testing: Verifying how different modules work together. Imagine testing how the bricks fit into the wall. System Testing: Putting it all together, ensuring the entire system functions as designed. Now, test the whole building for stability and functionality. Acceptance Testing: The final hurdle! Here, users or stakeholders confirm the software meets their needs. Think of it as the grand opening ceremony for your building. 2️⃣ 𝗡𝗼𝗻-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: ️ Performance Testing: Assessing speed, responsiveness, and scalability under different loads. Imagine testing how many people your building can safely accommodate. Security Testing: Identifying and mitigating vulnerabilities to protect against cyberattacks. Think of it as installing security systems and testing their effectiveness. Usability Testing: Evaluating how easy and intuitive the software is to use. Imagine testing how user-friendly your building is for navigation and accessibility. 3️⃣ 𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝘃𝗲𝗻𝘂𝗲𝘀: 𝗧𝗵𝗲 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗖𝗿𝗲𝘄: Regression Testing: Ensuring new changes haven't broken existing functionality. Imagine checking your building for cracks after renovations. Smoke Testing: A quick sanity check to ensure basic functionality before further testing. Think of turning on the lights and checking for basic systems functionality before a deeper inspection. Exploratory Testing: Unstructured, creative testing to uncover unexpected issues. Imagine a detective searching for hidden clues in your building. Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

💎 Accessibility For Designers Checklist (PDF: https://lnkd.in/e9Z2G2kF), a practical set of cards on WCAG accessibility guidelines, from accessible color, typography, animations, media, layout and development — to kick-off accessibility conversations early on. Kindly put together by Geri Reid. WCAG for Designers Checklist, by Geri Reid Article: https://lnkd.in/ef8-Yy9E PDF: https://lnkd.in/e9Z2G2kF WCAG 2.2 Guidelines: https://lnkd.in/eYmzrNh7 Accessibility isn’t about compliance. It’s not about ticking off checkboxes. And it’s not about plugging in accessibility overlays or AI engines either. It’s about *designing* with a wide range of people in mind — from the very start, independent of their skills and preferences. In my experience, the most impactful way to embed accessibility in your work is to bring a handful of people with different needs early into design process and usability testing. It’s making these test sessions accessible to the entire team, and showing real impact of design and code on real people using a real product. Teams usually don’t get time to work on features which don’t have a clear business case. But no manager really wants to be seen publicly ignoring their prospect customers. Visualize accessibility to everyone on the team and try to make an argument about potential reach and potential income. Don’t ask for big commitments: embed accessibility in your work by default. Account for accessibility needs in your estimates. Create accessibility tickets and flag accessibility issues. Don’t mistake smiling and nodding for support — establish timelines, roles, specifics, objectives. And most importantly: measure the impact of your work by repeatedly conducting accessibility testing with real people. Build a strong before/after case to show the change that the team has enabled and contributed to, and celebrate small and big accessibility wins. It might not sound like much, but it can start changing the culture faster than you think. Useful resources: Giving A Damn About Accessibility, by Sheri Byrne-Haber (disabled) https://lnkd.in/eCeFutuJ Accessibility For Designers: Where Do I Start?, by Stéphanie Walter https://lnkd.in/ecG5qASY Web Accessibility In Plain Language (Free Book), by Charlie Triplett https://lnkd.in/e2AMAwyt Building Accessibility Research Practices, by Maya Alvarado https://lnkd.in/eq_3zSPJ How To Build A Strong Case For Accessibility, ↳ https://lnkd.in/ehGivAdY, by 🦞 Todd Libby ↳ https://lnkd.in/eC4jehMX, by Yichan Wang #ux #accessibility

In all my years as a CISO, I’ve never experienced anything quite like this. What began as anxiety over social engineering, MFA weaknesses, and escalating AI threats… turned into one of the biggest surprises of my career. 🤯 Have you ever heard your workforce describe a security rollout as "life-changing" or "better than sliced bread"? 🍞 I didn’t think it was possible—until now. What’s even better? We achieved it while making the most significant leap in risk reduction I’ve ever seen. The secret? We went passwordless. 🙌 Here’s what we gained: 🔒 Unparalleled Security: We tackled the #1 threat—the driver of 80% of breaches—in one decisive move. This also gave us the first, best step toward Zero Trust. ⚡ Enhanced Productivity: We removed daily friction—no more password resets, lockouts, or clunky MFA. It’s saving time and costs, but most importantly: users love it. ❤️ 🤖 Future-Proofing Against AI Threats: We eliminated secrets/KBA from helpdesk calls. AI voice cloning and deepfakes can no longer trick people into giving away access... online or on the phone. But this isn't about what we did—it’s about what we all can do together. 🌎 Imagine a world where static credentials aren’t a risk. Cyber breaches would plummet. 📉 Identity theft and fraud would slow to a crawl. User trust and goodwill would skyrocket. 🚀 As a community, if we eliminated passwords, this could be our future. It wouldn’t be a utopia, but it would absolutely tip the scales in our favor. ⚖️ Let’s drive this transformative change together—and create a safer, better world for everyone. 🌐 💪 Big thanks to the ScrambleID team. Without your innovation, urgency, and partnership, we wouldn’t be here today. Next up: bots and AI agents! #Passwordless #Cybersecurity #Innovation #AIDefense #Equifax #ScrambleID