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ma accelerating decisions and shortening timelines 2 from tools to integrated discovery platforms eli lilly and company partnering with profluent goes a step further with ai designing new biological tools a platform for creating platforms underpinning multiple future therapies boehringer ingelheim expanding with openprotein ai shows ai embedding into antibody design where models learn from each experiment almirall expanding with the barcelona supercomputing center shows ai and supercomputing driving faster disease understanding and treatment design 3 data platforms and continuous learning systems gilead sciences expanding partnership with tempus ai shows pharma scaling ai driven real world data across oncology r d using multimodal datasets to improve trial design biomarkers and probability of success regeneron partnering with trinetx 300m patients highlights the shift to large scale data platforms combining clinical and genetic data to guide discovery sanofi partnering with kakao corp shows real world patient data becoming scalable ai for earlier diagnosis and personalised care gilead sciences expanding with tempus ai shows a shift from trial only evidence to continuous learning on treatment response 4 precision medicine and patient targeting daiichi sankyo us partnering with imagene ai shows ai better matching cancer patients to therapies using imaging molecular and clinical data 5 shaping how ai is built and used johnson johnson foundation and google org investing in ai training highlights the shift from building ai to deploying it where adoption skills and workflow integration are becoming the main bottlenecks 6 commercial models in dtx boehringer ingelheim handing commercial rights for ct 155 to click therapeutics inc while funding development shows pharma stepping back from dtx commercialisation while backing partners merck healthcare partnering with remepy shows the emergence of hybrid drugs combining pharmaceuticals with ai driven digital therapies into a single prescription product to enhance outcomes and differentiate treatment ai is no longer experimental in pharma it is becoming infrastructure and the constraint is shifting from technology to adoption and business models digitalhealth ai pharma more no more previous content no more next content 532 14 comments like comment share copy linkedin facebook x alan vanderborght ceo kybora 100 biotech deals closed across 5 continents guiding ceos to enduring success globally 1m miles flown building kybora into a 1b company 22 393 followers 10mo report this post 2025 kicks off pharma s steepest patent cliff in decades 25b in sales already losing protection and 300b more to follow here s how it s reshaping biotech m a more than 25b in u s drug sales from j j amgen and novartis will lose exclusivity this year when patents expire prices in the us the largest global market typically fall 80 90 within 12 months and this is only the beginning between 2025 and 2030 over 180 300b in branded revenue will vanish as drugs like keytruda eliquis and opdivo reach expiry companies such as amgen bms and merck face up to 60 of revenue exposure internal r d can t close that gap fast enough so leaders are using m a and platform deals to rebuild growth beyond 2030 here s why these deals look different pharma isn t buying single drugs anymore they re buying platforms and modalities rna in vivo delivery and precision biologics that can create multiple programs and absorb future cliffs late stage assets with clear payer pathways in cardio metabolic neuro oncology and immunology command the highest premiums deal structures now favor larger upfronts for de risked assets milestone based payments for early platforms and regional co development to accelerate launches look at some examples of recent deals shaping the trend 1 novartis avidity biosciences 12b acquired an rna antibody platform to sustain post 2029 growth a platform and manufacturing play not a one asset buy 2 amgen bms merck amgen s 27 8b horizon deal diversified its portfolio ahead of autoimmune and bone drug expirations enbrel prolia bms added karuna mirati and rayzebio to offset losses from eliquis and opdivo merck is investing in in vivo delivery before keytruda its top selling cancer therapy loses exclusivity bloomberg reports 20 major pharma biotech acquisitions this year alone here s what sellers should do be m a ready keep ip filings clean cmc validated and payer dossiers ready deals move fast when data rooms are current show scalability highlight how your platform generates multiple assets and reduces cogs buyers pay for repeatability not one offs structure for speed pre define tech transfer milestones and regional co dev options to align timelines and incentives the takeaways for executives 1 patent cliffs are the forcing function behind today s megadeals 2 the winners are buying platform capacity and late stage durability not one off assets 3 expect deal momentum through 2027 as pharmas prepare for 2028 2030 expirations rna in vivo delivery and precision biologics are the next safe harbors are you treating the cliff as an opening or a risk to survive is your platform scalable manufacturable and payer ready at kybora com we help biotech leaders align capital capability and story before the window closes more no more previous content no more next content 1 238 34 comments like comment share copy linkedin facebook x lance roberts lance roberts is an influencer chief investment strategist and economist investments portfolio management 21 623 followers 1y report this post one of the most concerning developments is the growing divergence between professional and retail investors institutional investors have quietly reduced risk shifting toward defensive sectors and fixed income while retail traders continue chasing speculative trades sentiment surveys confirm this imbalance showing extreme bullishness among small traders especially in options markets with these risks building under the surface prudent investors should proactively protect their portfolios no one can predict precisely when the market will correct but the ingredients for a sharp downturn are clearly in place savvy investors should use this period of complacency to reduce risk exposure before the cycle turns here are six practical steps investors should consider ️ rebalancing portfolios to reduce overweight exposure to technology and speculative growth names ️ increasing cash allocations to provide flexibility during periods of volatility ️ rotating into more defensive sectors like healthcare consumer staples and utilities that tend to outperform during corrections ️ reducing exposure to leverage by avoiding margin debt and leveraged etfs ️ using options prudently not for gambling but for protecting portfolios through longer dated puts on broad market indexes ️ focusing on companies with strong balance sheets stable earnings and reasonable valuations ️ the explosion of zero day options trading is not a sign of a healthy market it is a symptom of an unhealthy market increasingly driven by speculation rather than investment discipline retail traders have moved from investing to gambling chasing fast profits while ignoring the mounting risks greed is rampant leverage is extreme and complacency is near record levels markets can remain irrational longer than expected but history tells us these speculative periods always end in a painful correction bull markets do not die quietly they end with euphoric retail excess followed by painful corrections investors who recognize the signs early will avoid the worst of the fallout and be positioned to capitalize when value opportunities return more no more previous content no more next content 102 11 comments like comment share copy linkedin facebook x vlad gheorghe ai engineer dms open 4 445 followers 1y report this post tired of paying fund managers to make weird investment decisions and underperform the s p 500 claude can do the same thing for free i built a claude agent that manages a 100 000 paper trading account each day claude analyzes hundreds of websites to assess market conditions reviews stock performance executes trades and writes an investment memo explaining its decisions track claude s trading journey in real time https lnkd in erekvtcc here s how i built it hacked a simple agent in the claude app it has custom prompts memory with markdown files and uses the research capability to read hundreds of websites developed an mcp server using the alpaca api to let claude check its portfolio analyze stock data and execute trades built a streamlit dashboard showing real time portfolio performance and claude s investment diary though simple and built in just a few hours the agent works surprisingly well i m amazed by how easily you can set up an mcp server and add new capabilities to claude we re entering an era where creating complex integrated ai workflows is becoming remarkably straightforward check out my blog for the full technical breakdown and github code https lnkd in er7yt7zb what should i build next claude ai more no more previous content no more next content 237 36 comments like comment share copy linkedin facebook x aishwarya gupta aishwarya gupta is an influencer cmo helping brands grow build meaningful connections with consumers top voice brand management consumer research brand digital media strategist storyteller ex angel one paytm upstox tiktok 24 879 followers 6mo report this post for a long time anyone with confidence and a camera could pass off as a financial expert and the system quietly allowed it what looked like democratisation of advice was in many cases just unverified influence at scale high reach low accountability sebi stepping in with google changes that equation it begins to bring visibility and credibility closer together the addition of verification layers whether through app badges or simple checks like sebi s cvv framework starts to shift behaviour at the user level as well it nudges investors to pause verify and then act if apps are verified if violations are tracked if platforms are nudged to take responsibility the default experience for a retail investor improves fewer traps less noise better starting points that matters more than it sounds retail participation in india is growing fast but participation without guardrails creates cycles of excitement and regret and that s where trust breaks this move signals a clear direction for the ecosystem and puts some responsibility back into the system influencer finfluencer finance more 257 7 comments like comment share copy linkedin facebook x arpit bhayani arpit bhayani is an influencer 294 975 followers 8mo report this post most systems detect node or master failures using simple polling and while this approach sounds straightforward it has an interesting reliability issue the typical approach is to observe a node directly this usually means pinging it checking if a port is open or running a lightweight query to confirm it is alive on paper this seems fine but all of these methods share the same weakness what if the observer itself is wrong in a distributed setup network glitches are normal temporary packet loss routing hiccups or partial network partitions can easily make a healthy node appear unreachable to the observer the usual way to deal with this is to retry multiple times and declare failure after the n th consecutive failure this creates a classic tradeoff if n is small or polling happens frequently failure detection becomes fast but false positives increase a short lived network blip can trigger an unnecessary failover which can sometimes be more disruptive than the original issue if n is large or polling intervals are longer false positives decrease but real failures take longer to detect that delay directly increases downtime but there is a more reliable way to think about this problem when you already have a cluster of nodes available instead of relying on a single observer repeatedly polling a target node you can allow multiple nodes in the cluster to independently perform health checks the system then treats a node as failed only when a majority of observers agree that the node is unreachable this consensus based approach reduces the risk of false positives caused by network partitioning even if one observer loses connectivity the rest of the cluster can still provide an accurate view of system health consensus is costly so this approach is not the most cost efficient however it can be very useful if your system is large enough and distributed across multiple geographies more 222 10 comments like comment share copy linkedin facebook x milan jovanović milan jovanović is an influencer practical net and software architecture tips microsoft mvp 295 053 followers 1y report this post are you still polling your backend every few seconds just to get updates that s not just inefficient it s a terrible user experience in my latest video i walk you through how to implement real time communication in net using signalr we ll build an order tracking system that sends live updates from the backend to the frontend configure signalr in net build and secure your hub target specific users using user secure it with jwt update the ui instantly via websocket oh and i ll also show you how to make your signalr client methods strongly typed cleaner code fewer bugs watch the full tutorial here https lnkd in e6hh5gak if you re building modern web apps with asp net core this is a must more no more previous content no more next content 494 27 comments like comment share copy linkedin facebook x pavan belagatti technology leader ai evangelist developer advocate speaker tech content creator ask me about ai agents agentic engineering devops 104 507 followers 1y report this post can we use ai agents for stock market prediction recently llm based agents have demonstrated remarkable advancements in handling multi modal data enabling them to execute complex multi step decision making tasks this research introduces a multi modal multi agent system designed specifically for financial trading tasks the framework employs a team of specialized llm based agents each adept at processing and interpreting various forms of financial data such as textual news reports candlestick charts and trading signal charts the framework comprises four primary components the summarize module the technical analyst module the prediction module and the reflection module the summarize module condenses large volumes of textual news data into concise summaries that highlight factual information influencing stock trading decisions the technical analyst agent leverages the visual reasoning capabilities of llms to analyze candlestick charts with technical indicators providing interpretations for next day trading strategies the reflection module consists of two parts one assesses the short term and medium term performance of previous trades while the other plots past trading signals gene...