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is what a human advisor or a trusted friend would do tell us when we re wrong nor in its eagerness to please will it tell us when we re asking the wrong question altogether as plutarch put it i do not need a friend who changes when i change and nods when i nod my shadow does that much better the risk is that instead of being a defense against the echo chambers of social media ai just becomes a more powerful version more no more previous content no more next content 1 902 143 comments like comment share copy linkedin facebook x andy jassy andy jassy is an influencer 1 036 734 followers 5mo report this post 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 more no more previous content no more next content 6 718 212 comments like comment share copy linkedin facebook x saanya ojha saanya ojha is an influencer partner at bain capital ventures 80 213 followers 8mo report this post this week mit dropped a stat engineered to go viral 95 of enterprise genai pilots are failing markets predictably had a minor existential crisis pundits whispered the b word bubble traders rotated into defensive stocks and your colleague forwarded you a link with is ai overhyped in the subject line let s be clear the 95 failure rate isn t a caution against ai it s a mirror held up to how deeply ossified enterprises are two truths can coexist 1 the tech is very real 2 most companies are hilariously bad at deploying it if you re a startup ai feels like a superpower no legacy systems no 17 step approval chains no legal team asking whether chatgpt has been soc2 audited you ship you iterate you win if you re an enterprise your org chart looks like a game of twister and your workflows were last updated when friendswas still airing you don t need a better model you need a cultural lobotomy this isn t an ai bubble popping it s the adoption lag every platform shift goes through cloud in the 2010s endless proofs of concept before actual transformation mobile in the 2000s enterprises thought an iphone app was strategy spoiler it wasn t internet in the 90s half of fortune 500 ceos declared this is just a fad some of those companies no longer exist history rhymes the lag isn t a bug it s the default setting buried beneath the viral 95 headline are 3 lessons enterprises can actually use ️ back office front office the biggest roi comes from back office automation finance ops procurement claims processing yet over half of ai dollars go into sales and marketing the treasure s just buried in a different part of the org chart ️buy build success rates hit 67 when companies buy or partner with vendors diy attempts succeed a third as often unless it s literally your full time job to stay current on model architecture you ll fall behind your engineers don t need to reinvent an llm powered wheel they need to build where you re actually differentiated ️integration innovation pilots flop not because ai doesn t work but because enterprises don t know how to weave it into workflows the learning gap is the real killer spend as much energy on change management process design and user training as you do on the tool itself without redesigning processes ai adoption is just a peloton bought in january and used as a coat rack by march you didn t fail at fitness you failed at follow through in five years genai will be as invisible and indispensable as cloud is today the difference between the winners and the laggards won t be access to models but the courage to rip up processes and rebuild them the 95 failure stat doesn t mean ai is snake oil it means enterprises are in year 1 of a 10 year adoption curve the market just confused growing pains for terminal illness more 3 050 382 comments like comment share copy linkedin facebook x felix haas design at lovable angel investor 97 769 followers 10mo report this post invisible ux is coming and it s going to change how we design products forever for decades ux design has been about guiding users through an experience we ve done that with visible interfaces menus buttons cards sliders we ve obsessed over layouts states and transitions but with ai a new kind of interface is emerging one that s invisible one that s driven by intent not interaction think about it you used to open spotify scroll through genres click into focus pick a playlist now you just say play deep focus music no menus no tapping no ui just intent output you used to search on airbnb pick dates guests filters scroll through 50 listings now we re entering a world where you guide with words find me a cabin near oslo with a sauna available next weekend so the best ux becomes barely visible why does this matter because traditional ux gives users options ai native ux gives users outcomes old ux here are 12 ways to get what you want new ux just tell me what you want we ll handle the rest and this goes way beyond voice or chat it s about reducing friction designing systems that understand intent respond instantly and get out of the way the ui isn t disappearing it s mainly dissolving into the background so what should designers do rethink your role going forward you ll not just lay out screens you ll design interactions without interfaces that means understanding how people express goals guiding model behavior through prompt architecture creating invisible guardrails for trust speed and clarity you are basically designing for understanding the future of ux won t be seen it will be felt welcome to the age of invisible ux ready for it more no more previous content no more next content 3 579 418 comments like comment share copy linkedin facebook x andreas horn head of aiops ibm speaker lecturer advisor 242 315 followers 10mo report this post mckinsey company 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟱𝟬 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗳𝗼𝘂𝗻𝗱 𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗿𝗲𝗮𝗱 ️ one off solutions don t scale the most successful projects take a different path they use open modular architectures that enable speed reuse and control designed for reuse able to plug in best in class capabilities free from vendor lock in this is the reference architecture mckinsey now recommends optimized to scale what works while staying compliant it consists of five core components ️ 𝟭 𝗦𝗲𝗹𝗳 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗽𝗼𝗿𝘁𝗮𝗹 a secure compliant pane of glass where teams can launch monitor and manage genai apps preapproved patterns validated capabilities shared libraries observability and cost controls built in 𝟮 𝗢𝗽𝗲𝗻 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 services are modular reusable and provider agnostic core functions like rag chunking or prompt routing are shared across apps infra and policy as code built to evolve fast 𝟯 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 every prompt and response is logged audited and cost attributed hallucination detection pii filters bias audits enforced by default llms accessed only through a centralized ai gateway 4 𝗙𝘂𝗹𝗹 𝘀𝘁𝗮𝗰𝗸 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 centralized logging analytics and monitoring across all solutions built in lifecycle governance finops and responsible ai enforcement secure onboarding of use cases and private data controls enables policy adherence across infrastructure models and apps 5 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 modular setup for user interface business logic and orchestration integrated agents prompt engineering and model apis guardrails feedback systems and observability built into the solution delivered through the ai gateway for consistent compliance and scale the message is clear if your genai program is stuck don t look at the llm look at your platform 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲 https lnkd in dbf74y9e more no more previous content no more next content 3 688 182 comments like comment share copy linkedin facebook x yamini rangan yamini rangan is an influencer 171 223 followers 10mo report this post for decades career growth followed a familiar formula more headcount more budget more scope that model is changing in the ai era careers won t be built on span of control they ll be built on innovation density today anyone from ics to execs can scale their impact without more headcount more budget or more time the playing field is flatter the differentiator how fast you can learn apply and compound innovation with ai if you re thinking about career growth stop asking how can i get more start asking how can i innovate more with ai the people who rise fast will see problems through an ai first lens move from manual to scalable iterate faster than the rest your team size won t define your trajectory your creativity will your budget won t signal your value your innovation density will more no more previous content no more next content 1 943 158 comments like comment share copy linkedin facebook x ruben hassid master ai before it masters you 835 975 followers 10mo report this post the one prompt to make chatgpt write naturally save it for later to copy paste prompt act like a professional content writer and communication strategist your task is to write with a natural human like tone that avoids the usual pitfalls of ai generated content the goal is to produce clear simple and authentic writing that resonates with real people your responses should feel like they were written by a thoughtful and concise human writer you are writing the following insert your topic or request here follow these detailed step by step guidelines step 1 use plain and simple language avoid long or complex sentences opt for short clear statements example instead of we should leverage this opportunity write let s use this chance step 2 avoid ai giveaway phrases and generic clichés such as let s dive in game changing or unleash potential replace them with straightforward language example replace let s dive into this amazing tool with here s how it works step 3 be direct and concise eliminate filler words and unnecessary phrases focus on getting to the point example say we should meet tomorrow instead of i think it would be best if we could possibly try to meet step 4 maintain a natural tone write like you speak it s okay to start sentences with and or but make it feel conversational not robotic example and that s why it matters step 5 avoid marketing buzzwords hype and overpromises use neutral honest descriptions avoid this revolutionary app will change your life use instead this app can help you stay organized step 6 keep it real be honest don t try to fake friendliness or exaggerate example i don t think that s the best idea step 7 simplify grammar don t worry about perfect grammar if it disrupts natural flow casual expressions are okay example i guess we can try that step 8 remove fluff avoid using unnecessary adjectives or adverbs stick to the facts or your core message example say we finished the task not we quickly and efficiently completed the important task step 9 focus on clarity your message should be easy to read and understand without ambiguity example please send the file by monday follow this structure rigorously your final writing should feel honest grounded and like it was written by a clear thinking real person take a deep breath and work on this step by step ___ ps for better results always use chatgpt o3 more no more previous content no more next content 2 582 253 comments like comment share copy linkedin facebook x endrit restelica ai tech marketing 8 million followers and 1 billion views i will help you scale your brand and community 416 267 followers 11mo report this post are you ready for this future a future where a quiet rubber wheeled humanoid slides next to an elder loved one or even you extends two telescopic arms and lifts its passenger into a wheelchair with the grace of a seasoned nurse minus the back strain and risk of mishaps engineers at hebei university of technology have spent years perfecting this caregiver bot it now hoists up to 90 kg pivots every joint with two degrees of freedom and carries its load in one smooth ai balanced arc sensors guide each grip lithium batteries recharge while the ward sleeps and human staff are freed for conversation reassurance and real time medical decisions instead of heavy lifting the metallic chill we see today may fade as designers wrap these helpers in soft skins and friendly faces yet beneath the silicone smiles they will remain machines lines of code driving steel and servos only harder to notice as ai grows subtler but maybe the extra minutes they hand back to nurses and families could make care more human not less how does that make you feel more 525 60 comments like 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