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Text of the page (random words):
olesale price in q2 battery charging and discharging set prices in 36 of nem dispatch intervals up from 17 a year earlier during the evening peak battery discharge was the dominant price setter the curve is therefore being flattened from both directions more demand when solar is abundant and more supply when evening demand rises as battery capacity grows the gap between the cheapest and most expensive hours is narrowing that means lower peak prices less volatility and a more productive use of daytime solar more no more previous content no more next content 2 456 169 comments like comment share copy linkedin facebook x nikhil mittal cto zepto building india s fastest commerce engine writing about engineering at scale 63 344 followers 2mo report this post our frontend builds crossed 20 minutes the obvious move was to rip out webpack for something faster we didn t before touching a single line of config we profiled the entire pipeline webpack s progressplugin turned up something we didn t expect one plugin terser was eating 13 minutes of every build webpack wasn t the bottleneck javascript minification was that one number changed the whole plan we kept webpack replaced babel with swc swapped terser for esbuild moved css optimisation to lightningcss parallelised compilation across cores with thread loader ️ fixed a hidden bottleneck too aws codeartifact tokens were re authenticating every 12 hours and quietly busting our docker cache restructured the dockerfile into layers that actually cache 20 minutes to 2 minutes cold build 90 faster incremental 95 faster across 20 production apps 600 prs a month 5 000 ci runs no migration no four week rewrite no new bundler to debug for the next year the real lesson wasn t about tooling it was that we almost skipped measurement and went straight to migration the industry default glad we didn t full breakdown the speed measure plugin data the pnpm switch the exact dockerfile changes link in the first comment really proud of what our engineering team pulled off here worth the read zepto zepto tech zeptotechxpress zeptoengineering more no more previous content no more next content 3 355 93 comments like comment share copy linkedin facebook x jan rosenow jan rosenow is an influencer professor of energy and climate policy at oxford university senior associate at cambridge university world bank consultant board member linkedin top voice fei frsa 133 305 followers 2mo report this post every january the same headlines ev sales are slumping nobody wants electric cars the bubble has burst and every year the data tells a different story in 2015 battery electric cars made up 0 3 of new uk registrations the 12 month average now stands at 25 1 one in four new cars registered in the uk is fully electric so why do the collapse stories keep coming because of a predictable seasonal pattern manufacturers push ev registrations into december to meet fleet targets and the zev mandate january then looks weak by comparison it happens every single year the spike and the dip are two halves of the same mechanism but the december peaks are becoming less exceptional in 2022 december s ev share stood 16 percentage points above that year s average in 2025 just 6 points the baseline has caught up with the peaks and june 2026 came in at 29 9 within touching distance of december levels in an ordinary month the rolling average has climbed for a decade without a single sustained decline you can question the pace as being too slow but you cannot question the direction of travel as the data is pretty clear next time you read that nobody wants evs check the trend line over the last decade rather than a single month data dft vehicle licensing statistics 2015 to 2025 new automotive 2026 more no more previous content no more next content 1 862 153 comments like comment share copy linkedin facebook x arthur mensch arthur mensch is an influencer cofounder ceo mistral 266 442 followers 3mo report this post of course you need to use open source models if you re an enterprise leader close model providers that are now forcing data retention are gaining immense leverage on your business if you don t as you connect models to your business context they see it and learn from it and have a track record of going after their most successful customers thanks to this information but that s not enough you also need to store your data and records in open systems or your software vendors might block you from building ai systems outside of the walled garden they have set up for you if you can t convince them to give you complete access to the data they manage for you ai fortunately allows you to migrate quite fast once you ve got hold of your data you ll need to manage how ai systems can access this data on behalf of human users because you don t always want bob to see what alice is doing in your company that s hard and merciless since ai models are great at finding need to know errors it takes systems that check hard access rules and models that check soft access rules now comes the most important part you need to set up your own continuous training flywheel so that you can improve your ai systems based on their interaction with your employees and your users this is how you turn the edges of your business into ai systems your vendors and competitors cannot replicate it s also how you reduce deployment cost as well as you can shrink models according to model input distribution those bills are getting substantial we need to collectively become efficient if we want ai development to continue so that matters all of these efforts might seem daunting they are this is both a complete replatforming of your it and a complete change in the way you re developing software and operating your business ai lifecycle management requires understanding human behavior and gradient descent that s a stretch at mistral we facilitate that work by providing all primitives that you need in a single control plane studio and a training platform forge with our applied ai engineers and scientists working hand in hand with our customers we ensure that we transfer knowledge and that we can disappear once the systems are up and running we deploy on our customers infrastructure or through our zero data retention hosted services so that your edges remain your edges and the switch button can be fully in your hand frontier ai can accelerate the growth of your business but if it s not in your hands it s not going to be your growth more 4 672 246 comments like comment share copy linkedin facebook x sean connelly sean connelly is an influencer architect of u s federal zero trust co author nist sp 800 207 cisa zero trust maturity model former cisa zero trust initiative director advising governments enterprises 24 162 followers 4mo report this post zero trust for ai agents anthropic just released zero trust for ai agents as we re thinking about agentic permissions applying a zero trust discipline is critical to secure adoption ai agents interpret goals call tools chain actions delegate to other agents and maintain context across sessions the trust surface is different the paper introduces least agency a concept that owasp has been promoting and the distinction from least privilege is worth sitting with least privilege asks what an identity can access least agency asks what an agent can do under what conditions with which tools and with what level of oversight autonomous agents introduce action risk alongside access risk and the boundaries around behavior need to be architecturally enforced not assumed the paper includes a design test worth writing down does the control make the attack impossible or merely tedious the practical controls follow directly from zero trust fundamentals cryptographic agent identity short lived credentials tool allow listing sandboxed execution and full traceability from prompt to action to outcome none of this is new doctrine it s existing architecture applied to a harder problem full disclosure the paper cites nist sp 800 207 on zero trust architecture and the cisa zero trust maturity model both of which i co authored during my time supporting federal zero trust efforts at cisa zero trust is built for a world where we have to remove implicit trust agentic ai is the next version of that same problem valid identities valid credentials legitimate looking actions and still no basis for assumed trust access is earned actions are constrained agency must be governed link to anthropic s paper in the comments more no more previous content no more next content 1 101 135 comments like comment share copy linkedin facebook x vineet agrawal vineet agrawal is an influencer 30 revenue for healthcare startups in 3 6 months 50 million generated for clients with ai implementation 59 600 followers 4mo report this post saudi arabia built the world s largest virtual hospital and we haven t even heard of it it connects 224 hospitals and treats 400 000 patients a year without a single physical bed it s called seha virtual hospital in riyadh and it just earned a guinness world record for being the largest virtual healthcare provider in the world but how can a hospital be virtual how does it work imagine you live in a small town with only a basic local hospital it has doctors and equipment but if you need a cardiologist or neurologist you travel 6 to a bigger city in urgent situations people lose lives with seha specialists treat you remotely through your local hospital reviewing scans diagnosing conditions prescribing treatment while local staff execute it that s the model specialist expertise delivered through existing hospitals and here s what makes it work ️ ai prioritizes urgent cases analyzes ct scans and imaging to rank who needs immediate intervention ️ iot monitors patients remotely heart failure patients wear devices that alert doctors before hospitalization is needed ️ integrated health records manages prescriptions and reports across all 224 hospitals in real time the results icu patients now stay an average of 4 days instead of weeks stroke patients get ct scans within 25 minutes of arrival treatment starts in 28 minutes radiology reports in 2 hours this isn t telemedicine where you video call a doctor from home this is expertise delivered through your local hospital without the specialist being physically there it proves you don t need cardiologists and neurologists in every town you just need good internet and hospitals willing to collaborate do you think virtual hospitals could solve specialist shortages in rural areas entrepreneurship healthtech innovation more no more previous content no more next content 1 195 127 comments like comment share copy linkedin facebook x melissa rosenthal melissa rosenthal is an influencer turning companies into the voice of their industry with owned media co founder outlever ex cco clickup cro cheddar vp creative buzzfeed 53 875 followers 4mo report this post every ai company serving your org is currently losing money on you the compute costs 10x what they re charging which means we re all building on borrowed pricing claude pro costs 20 month the compute to serve a power user runs 200 400 month microsoft was losing 80 month on some copilot users paying 10 what does that mean they re buying market share with subsidized pricing your teams build ai into everything it becomes infrastructure then prices go up and you can t leave and this is already happening github moved copilot to usage based billing last month openai launched a 100 month tier anthropic s top plan is 200 month microsoft raised 365 prices twice in four years and pointed directly at ai costs meanwhile most companies are still budgeting like 20 seat is the real price a 50 person team on claude pro costs 1 000 month on paper at actual token rates that same usage costs 15 000 to 40 000 month agentic ai made this worse coding agents don t use tokens like a chat conversation they burn 10x more github literally said this is why they killed flat rate pricing openai and anthropic are both heading toward ipos public investors don t tolerate selling a dollar of compute for twelve cents the moment these companies go public the subsidies will likely die your finance team needs to audit actual token consumption across every team not seats not licenses actual usage then model what happens when prices go up 5x the gap between what you pay today and what you ll pay in 18 months is going to be one of the ugliest budget surprises most companies have ever absorbed read the full piece on state of brand here https lnkd in gicbt hw more no more previous content no more next content 2 151 286 comments like comment share copy linkedin facebook x oron gill haus oron gill haus is an influencer 47 499 followers 4mo report this post the ai coding revolution is here but are we testing for it as ai assisted development reshapes how we build software i ve been thinking a lot about something that is talked about often but doesn t always get the focus it deserves automated testing at jpmorganchase we re embracing ai coding tools to accelerate delivery reduce toil and empower our teams to focus on the work that matters reducing cognitive load of repetitive tasks but speed without safety is just risk in disguise here s what i believe every leader and this is broader than technology needs to consider right now ai writes code faster than humans can review it manually if your testing strategy is still largely manual you re already behind ai generated code can introduce subtle logic errors security vulnerabilities or edge case failures that look perfectly reasonable on the surface automated testing is no longer a best practice it s a non negotiable safeguard test coverage is your new quality contract when ai is your co developer the test suite becomes the specification if you can t describe expected behavior in a test you can t trust what the ai builds investing in robust unit integration and regression testing frameworks is investing in the integrity of your entire delivery pipeline shift left testing amplifies ai s value it doesn t slow it down some worry that rigorous testing will negate the speed gains from ai coding the opposite is true when automated tests are embedded early in the development lifecycle ai tools can iterate faster self correct and validate outputs in real time testing enables velocity it doesn t constrain it your teams need to evolve alongside the tools the best teams of tomorrow won t just write code they ll architect test strategies evaluate ai outputs critically and build systems that are observable and verifiable by design we owe it to our teams to invest in this skill evolution now at the scale we operate serving millions of customers the cost of a defect isn t just technical it s trust and trust once broken is hard to rebuild ai is a force multiplier but multiplying without a strong foundation multiplies risk just as fast as it multiplies output build fast test smarter ship w...