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Tech leads shares higher in Asia as oil slips
By Wayne Cole SYDNEY, Sept 21 (Reuters) - Share markets edged higher in Asia on Monday as AI's insatiable demand for data buoyed chipmakers, and oil eased on reports more
Ube MKE: Child-run small business brings Filipino-inspired sweet treats to Milwaukee
A child-run small business is bringing Filipino-inspired sweet treats to Milwaukee and the Shorewood Farmers Market.
Knoxville shoppers support small businesses across the country at Vintage Market Days
Vintage Market Days of Knoxville returned to the Expo Center for three days of the community supporting small businesses and nonprofit organizations.
Knoxville shoppers support small businesses across the country at Vintage Market Days
Vintage Market Days of Knoxville returned to the Expo Center for three days of the community supporting small businesses and nonprofit organizations.
KIII 3 News
KIII 3NEWS is the ABC affiliate in Corpus Christi, Texas, operating to serve, inform and improve communities in the Coastal Bend. The station began broadcasting in South Texas on May 4, 1964. Today, KIII 3NEWS dominates one of the fastest-growing markets in Texas. The station’s service to the community and commitment to local news and weather coverage has consistently made it the Coastal Bend's news leader for decades.
Why Is Elbit Systems (TASE:ESLT) Rebranding Its U.S. Business As Twenty Six Defense?
Elbit Systems (TASE:ESLT) has rebranded its U.S. operating segment as Twenty-Six Defense, targeting the domestic American defense market. The new Twenty-Six Defense banner is intended to highlight U.S. industrial capacity and onshore production for defense customers. Elbit Systems links the rebrand to closer alignment with U.S. national security priorities and government focus on localized manufacturing. The Twenty-Six Defense shift toward a U.S. centric identity is an important context for...
Marketing Daily: AI Visibility Sources Vary Widely By Health Category: Report
To gain more visibility in AI search results, health & wellness CPG brands need to focus on different content strategies depending on product category,
Gemini’s exchange business is shrinking. Its regulatory licenses may be the real prize
The crypto platform’s market value has fallen to about $753 million, putting fresh attention on the licenses, custody infrastructure and customer relationships a buyer could inherit.
A big deal for Generac, a new public market, and more Milwaukee business news
Milwaukee Business journal editor-in-chief Tim Gibbons talks with WTMJ's Adam Roberts about a big deal for Generac, a new public market in Brookfield, and
Williams-Sonoma's stock has soared in a sluggish housing market. Here's how it won over Wall Street
Williams-Sonoma is outperforming the furnishings industry due to its ability to grow profitability despite a weak housing backdrop.
How This Founder Turned Her Side Hustle Into a $500,000 Business
Former Survivor cast member Liz Wilcox was working as a copy editor when she had the idea to start selling email templates.
Housing market shifts in buyers' favor as more sellers offer concessions
More sellers are offering concessions and cutting prices to entice buyers who have been sidelined by high mortgage rates and record-high prices.
Dealroom.co | Great Hill Partners: middle-market growth buyouts across software, financial services, healthcare, consumer and business services
What's the deal?Great Hill Partners is a Boston- and London-base…
Bristol, Blue Star ‘breakup’ has Ivywild hallways abuzz
Bristol Brewing Co. and Blue Star restaurant marked the beginning of a beautiful business partnership almost 30 years ago, inside neighboring operations, in co-owned property near the intersection of South Tejon Street and East Ramona Avenue. When the historic former Ivywild elementary building went on the market just blocks away at 1604 S. Cascade Ave., […]
Peters' properties under foreclosure are on the market for over $42 million
Most of the properties are located in the downtown area, while another one is situated at the corner of the busy Cerrillos Road-Rodeo Road intersection.
‘Skyrocketing’ diesel prices impacting area businesses
The average price of diesel fuel in the Scranton/Wilkes-Barre/Hazleton market rose to $6.52 as of Thursday — up about $2.50 from last year and nearing the record high of $6.62 set in May 2022.
Lake City hosts Crossroads Music and Market Festival with live music, food and family fun
Downtown Lake City turned into a hub of music, food and family fun Saturday as the Lake City held its annual Crossroads Music and Market Festival.
China keeps benchmark lending rates unchanged for 16th month in September
China kept its benchmark lending rates unchanged for the 16th straight month on Sunday, in line with market expectations.
What’s Your Hottest Business Take?
Let us know your hottest business takes in the comments.Subscribe to The Hustle:🔗 https://clickhubspot.com/vu3lGet the 5-minute newsletter keeping 2M+ innovators in the loop🔗 https://clickhubspot.com/3klnAbout HubSpot: HubSpot is a customer platform that provides education, software, and support to help businesses grow better. The platform includes marketing, sales, service, commerce, operations, and website management products that start free and scale to meet our customers’ needs at any stage of growth. Today, thousands of customers around the world use HubSpot’s powerful and easy-to-use tools and apps to attract, engage, and delight customers.#TheHustle #HubSpot
Despite rain, attendance strong at yearly south-side business fair
Santa Fe has made efforts to support immigrant-owned businesses, one sign of which is growth in markets like the Feria Southside.
Cardinal Health Targets 13%-15% EPS Growth as Specialty Momentum Builds
Cardinal Health (NYSE:CAH) CEO Jason Hollar said the company expects fiscal 2027 to extend the broad operating momentum it generated in fiscal 2026, with all five operating segments positioned for what he described as a “constructive” period. Speaking at the Morgan Stanley Global Healthcare Confere
Oscar Health (OSCR) Bets On Lucy As CEO Calls Time On Employer Coverage
Oscar Health (NYSE:OSCR) introduced its AI-driven Lucy Healthcare Marketplace, a digital hub for carriers, brokers, and consumers. The company presented Lucy as part of a shift toward individual coverage options, including support for ICHRA-based benefit designs. Chief executive Mario Schlosser said the traditional employer-sponsored health insurance structure is ending, pointing to alternative coverage models. Oscar Health’s Lucy Healthcare Marketplace launch and public push toward...
Marketing Daily: AI Visibilty Sources Vary Widely By Health Category: Report
To gain more visibility in AI search results, health & wellness CPG brands need to focus on different content strategies depending on product category,
Delivery Outlook Shows Tesla (TSLA)’s Core Business Under Pressure. Its Next Growth Engine Must Deliver
Goldman Sachs has cut its delivery forecast for Tesla, Inc. (NASDAQ:TSLA) while citing weaker sales trends across the company’s most important markets. The investment bank slashed its Q3 delivery forecast to 435,000 vehicles from 490,000. It also lowered its Q4 delivery forecast to 475,000 vehicles from 515,000. The question now is whether Tesla’s demand weakness […]
Cardinal Health Sees Growth Normalize as Specialty, At-Home Investments Expand
Cardinal Health (NYSE:CAH) CEO Jason Hollar said the company’s fiscal 2026 performance was supported by double-digit earnings growth across each of its five operating segments, while fiscal 2027 guidance reflects continued growth from a more normalized base. Speaking at a Baird event, Hollar said t
The Big Holes in Wearable Heart Rate Variability And Readiness Scores
On September 9th, Apple announced it was revamping its Apple Watch Health Sensing System, rolling out a Readiness score (0 to 10), and increasing the frequency of heart rate variability (HRV) outputs 24-fold. This can be viewed as upping its competition with various other consumer wearable sensors. These “readiness scores” as a composite of multiple metrics, with heart rate variability (HRV) being front and center for most. The majority of Americans are now using wearable sensors, which equates to well over 100 million adults. HRV and Readiness scores are increasingly being marketed as a measurement of autonomic nervous system health, a digital marker for future disease, a clock for biological age, and a holistic metric to promote healthspan and even longevity. (Apple also introduced a new longevity tab and “Health Age”.) None of this has been proven. In this edition of Ground Truths I am going to review what we know about heart rate variability and readiness scores. Heart Rate Variability HRV is the variation in normal heart cycle timing. The variability of the heart rate, the barely perceptible millisecond changes in time between consecutive heart beats (see R-R intervals in the Figure below, left panel), is due to interplay between the sympathetic and parasympathetic (vagal nerve) inputs. Distinct from heart rate, individuals with the same heart rate can have very different HRVs. It is a rough reflection of the autonomic nervous system (ANS) activity, inadequate to say whether a person’s ANS function is abnormal. For more than three decades, heart rate variability (HRV) has been measured and several studies have found an association of low HRV and clinical outcomes, particularly a link with higher all-cause and cardiovascular mortality. There have also been less well established links of low HRV to risk of early cognitive impairment, dementia, mental illness, Type 2 diabetes and substance abuse. An important reminder is that HRV is a surrogate marker without any established cause-and-effect relationship. If you increase your HRV, that doesn’t mean it will improve health outcomes. In fact, there is no hard evidence for that. All that work linking to health outcomes was done with electrocardiogram (ECG) derived HRV. Now, in the era of consumer wearables, this is getting assessed differently, by optical pulse (yes, the lights you see) plethysmography (PPG) or what is called pulse rate variability (PRV). They are not the same, as shown below (right panel) and only concordant when the delay between the ECG and pulse is kept constant, which basically means at rest. I should mention there’s also what I will call MPV, a mattress mechanical movement sensor, a derived heart rate variability, that companies like Eight Sleep use, even further away from directly measuring HRV. HRV has not one uniform measurement but many different types of quantification, such as RMSSD, the magnitude of difference between successive R-R intervals of normal sinus beats (N-N) or SDNN, the standard deviation of NN intervals, both in milliseconds. SDNN is one of the so-called frequency domain HRVs (others are LF, HF, LF/HF). Different wearable sensors use different metics; Apple has relied on SDNN and nearly all of the others use RMSSD, which is generally considered the more accurate metric. There’s also the different length of time measured, such as for a matter of minutes, all day, or an overnight’s sleep. Short measurements are especially problematic since they don’t capture enough of respiratory modulation and other factors that influence HRV. Share Ground Truths How well does HRV correlate with PRV? There are very limited studies, especially independently done. One that is commonly cited was conducted by Air Force researchers in only 13 healthy adults assessing Oura ring 3 and 4, Whoop 4.0, and Garmin Fenix 6 and showed a correlation coefficient of 0.88 to 0.97 and a mean absolute percentage error from 6 to 10%. The correlation is not a perfect 1.0, but there’s at least a fairly high level of correlation. HRV is supposed to increase during the night due to takeover of the parasympathetic nervous system, and higher during deep sleep. A recent example of my 1 week, all day “HRV,” and one during sleep is shown below. As you can see, the N of 1 data are inconsistent for the same days from different sensors (Oura, AppleWatch, Fitbit Air, and Eight Sleep) by patterns, absolute numbers, and comparison with prior days and weeks. The largest study in over 8 million Fitbit users (the old version, not Google Fitbit Air, introduced in May 2026) gives you a sense of the effect of age, sex, and the 2 different main HRV (here PRV) metrics, with RMSSD on the left and SDRR (=SDNN) on the right below. That study, from data collected in 2018, is a major outlier, since all the more recent ones are tiny with respect to sample size. Many of the companies have not had independent evaluation of their HRV, such as Eight Sleep, but have published a low standard error on their website. There are some other published studies on the correlation between HRV and PRV, but they are all small and only in healthy adults. A scoping review emphasized the lack of study in underrepresented individuals, including the aged, people of color (which affects the PPG signal), and individuals who are underweight or obese. Add the typical adult age 60 plus with one or more chronic diseases. For example, one study in over 900 adults found poor correlation of HRV and PRV, non-uniformly underestimated across many chronic diseases (cardiovascular, endocrine, neurological, respiratory, and others), concluding PRV is “an invalid surrogate for HRV.” A recent systematic review of 43 studies comparing HRV and PRV found reasonable pooled absolute standardized error (HRV as gold standard) but only 10 of the studies provided quantitative synthesis in ideal conditions. Their main conclusion was similarly cautious: “PPG-derived HRV [PRV] should not be regarded as universally interchangeable with ECG-derived HRV across all devices, populations, and recording contexts.” Factors Affecting HRV and PRV That gets me to the long list of factors that affect HRV (and PRV) besides the device, the type of measurement (RMSSD, SDNN or others), the person’s signal, the sensor site, the duration of data capture, if weighting by sleep stage is used, how artifact is processed and corrected. And this list is not complete!: Oura puts out data from their community of users (who input data) on what affects their overnight HRV. The factors currently provided are: no alcohol (increase 8%), melatonin (increase 2%, float tank (increase 2%), wine (decrease 4%), and party (decrease 14%) in overnight HRV. Must be some big parties! Subscribe What is a PRV measurement good for? It has been falsely characterized as an index of “autonomic balance” and a specific indicator of stress. A 2018 review of the studies available for HRV and its relationship to stress, not using any of the current wearables, found that stress can lower HRV. But so can many other factors. The non-specificity of the signal, indexed to the table above, is striking. Evidence from a UK Biobank study of over 46,000 participants with actual HRV looked at genetically predicted HRV, a genetic risk score, that failed to show the expected HRV-mortality link, indicating that _HRV is likely not causa_l, but rather a reflection of person’s physiologic state. A review of consumer wearable HRV data from 5 longitudinal studies showed that nighttime PRV was not associated with perceived stress, and surprisingly higher HRV, in the largest cohort (N=717 participants), was correlated with higher stress. An Oura ring cohort of 525 first-year college students found a link between overnight PRV and perceived stress, but that was also seen with resting heart rate, sleep, and respiratory rate. Several very small studies have examined the relationship of HRV and athletic injuries or guiding training with mixed, and predominantly negative results. HRV biofeedback training with paced breathing had no significant effect on reducing stress or raising HRV, as demonstrated with sham controlled trials. When HRV for multiple days showed a decline in conjunction with body temperature, the Oura ring published data for prediction of Covid. The WHOOP company sponsored an observational study, published in 2026, of 30,000 users for 72 weeks, without a control group, that reported reduced alcohol intake (5.8 % points) by self-report. That doesn’t tell us much, and particularly about the merits of HRV for behavioral change. If you use the same device and conditions as longitudinal trends for multiple (at least 2-3) weeks that may be the one way to get something useful from the measurements. Data for overnight sleep with minimal motion and using RMSSD is the best proxy for real HRV. The reason to look at trends rather than any given night is that it more likely represents something, even though you won’t know with certainty what the “it” is. Keep in mind there are no data, no peer-reviewed evidence, to show that HRV fluctuation in-person has any correlation with health outcomes. Share Readiness Scores These are proprietary scores that integrate different metrics for each of the wearables: no algorithms have been disclosed. They are unvalidated against health outcomes. In a review of 14 composite health scores of readiness and recovery, HRV contributed 86% to the scores, followed by reading heart rate (79%), physical activity and sleep duration (both at 71%). That review noted the substantial variability in measurement protools and lack of standardization. Sleep staging is notoriously inconsistent and inaccurate by these sensors, which adds further to the HRV uncertainties for what the scores, which use sleep stage data, mean. Only resting heart rate has been shown consistently across devices to be extremely accurate. I’ve made a Table to summarize what we know about which metrics are included, the scores, any peer-reviewed studies that compared the readiness score with health outcomes, and the corresponding (if any, NA-not available) citation. You will note that some companies do not use the term “readiness," such as WHOOP for recovery, and Garmin, which has 2 different scores, one of which is Body Battery. Eight Sleep uses the term “Fitness Score.” They all include HRV; Apple includes a new metric they call “Recovery HRV” which among other components uses 7-days of sleep, but it is unclear what this means or how it is differentiated from other scores (there are clearly no data for outcomes). We have no knowledge of how the different components are weighted or whether any of these scores are better than resting heart rate, HRV alone, physical activity, or any other single metric. Since none of these are standardized, they are not interchangeable, so if you get a 90 for Oura that has no relationship to a 90 on a Google Fitbit Air. Notably, the company can update its algorithm for readiness score at any point without notification to device users. Without any useful evidence of actionability for these scores or established relationship with health outcomes, it is hard to make a case for their value. At the Apple recent announcement they showed their Readiness score (0-10) on the watch (Figure below) but there are no published data on this score, not even on their website. It’s available only on their new Watch Series 12 or Ultra 4 [of course, ;-)]. That exemplifies the problems with these scores, lack of data and evidence for being meaningful to promote health. Perhaps the best study (which isn’t saying much) is the WHOOP Recovery for golfer performance, because it did correlate with an objective outcome, even though there was no control group and the authors were all from the company. Among the 389 pro golfers, an absolute 10-per cent point increase in Recovery score was associated with about 0.5 fewer strokes per round. But that’s hardly a health outcome! WHOOP is also conducting a study in over 2,700 runners to see if their recovery score will be linked to less injuries and improved performance, but that is not yet published and has no control group or randomization. Putting This in Context For two decades I’ve been enthusiastic about the potential for digital health and particularly wearable biosensors. Over the years, we’ve seen some great progress for their ability to promote physical activity and accurately detect atrial fibrillation (the first FDA cleared deep learning AI for consumers). That work was the subject of rigorous research. But there are holes in the data and evidence for other metrics. One notable one is the “VO2 max” story that I wrote about earlier this year. At that time many subscribers asked me to cover heart rate variability, which I finally got to here. When I dived into the research and publication for HRV and readiness scores, I expected to find at least some that were of high quality and demonstrated their utility by linkage to health outcomes. To my surprise, I found none. The wearable sensor measurements for HRV (PRV) are, for the most part, accurate, but that validation work has only been done in small studies of healthy adults and does not take into account the long list of factors, from the device, software side, and the user side, that affect HRV measurements. Moreover, this metric chiefly relies on optical sensing and, as we have learned for heart rate PPG sensing, may be less accurate in people of color. Keep in mind that all of the health outcome association evidence comes from ECG-derived HRV; none are from wearable sensor data. I will repeat the key point: there's no peer-reviewed evidence to show that in-person HRV fluctuation—or efforts to raise your HRV— has any correlation with health outcomes. For those of you who look at your HRV on awakening, or even 2+ week trends of it being low, I hope this context helps to relieve any anxiety. Yes, low HRV (not PRV) has been shown to increase risk of some diseases as summarized above. But efforts to raise your HRV—a surrogate metric— has not been established for improving any health outcomes. HRV does not have any evidence of causality (the genetic evidence actually goes against this possibility). In this summary, I have not included data for other wearable sensors such as Polar, Samsung, Withings, Suunto, Amazfit, Coros, Ultrahuman, or additional mattress sensors. These are beyond my first-hand experience, and as far as I know from my in-depth review none have any peer-reviewed published data that differ from the 6 sensors I’ve reviewed here. From the points I’ve gone over above, we’re not ready for readiness scores. Besides being proprietary, they are predominantly based on metrics that have their own issues. It’s compounding the problem, like building a house without a solid foundation that has never undergone a rigorous inspection, and then selling it. Like I mentioned for PRV, you can look at trends over weeks rather than any single day, to get a handle, but even that may not be helpful. There’s simply no evidence that these scores meaningfully relate to health outcomes. I’d emphasize they might, but that requires doing prospective or randomized studies to prove it. None exist. There’s great promise for HRV/PRV utility_. For example**,**_ Prof Maiken Nedergaard, who discovered the brain glymphatics that are essential in eliminating metabolic waste products from the brain during sleep, has posited that HRV could be a non-invasive marker for neuromodulator oscillations, brain-body regulatory circuits, and brain clearance. That would be extremely useful, but like everything else on HRV and readiness scores it requires solid research and validation. The lay media isn’t helping much to get the story straight. Earlier this year The Economist published a piece entitled “The most useful indicator of your overall health” which ordained HRV as an “accumulated stress score.” That’s akin to the false assertion about VO2max: “V02 max is the singular most powerful marker for longevity.” As I’ve summarized here, that is not established. The fact is that so many things can lower HRV, including physical exercise (especially an intense workout), reduced sleep quality, stress, the list above, no less the device, signal, and software. Whatever fluctuations observed have not been correlated with any health outcome. Sadly, “datamaxxers” are widely using HRV and readiness scores that have never been validated to mean anything. We already know that for some people using the sensors for sleep metrics, it can induce “orthosomnia,” an obsession to get high sleep quality, with associated high levels of anxiety. In an experiment done by a company to promote sleep quality for its employees, “For those employees who did use the trackers, many reported feeling perfectly rested until their tracker told them they had had a terrible night. Others were told that they had slept like a baby when they had actually been lying awake worrying about the quality of their sleep. “ The same problem can result from preoccupation with HRV or readiness scores, with anxiety that would lead to further reduction in both. It you are using a wearable like >100 million American adults, it’s OK to look at these data, but contextualized with the major caveats reviewed here. If you are one to require evidence that HRV or readiness scores are linked to health outcomes, you may not even want to look. The companies make it hard to turn them off! Let me end with the companies that make and sell wearables. Apple’s doubling down on HRV (24-fold more reporting and heart rate very 5 seconds) and introduction of a Readiness score tells us that consumers have bought into these metrics and they are joining the club. However, all of this is occurring with a backdrop of tens millions of users, claims about the data that are not backed up by adequate evidence, marketing way out in front of whatever limited data exists, and not being transparent about their readiness score algorithms. The companies can well afford to do the research that is needed to connect these metrics with health outcomes show, once and for all, that increasing HRV or using readiness scores promotes our health. If they believed and invested in the products they are selling, we’d not be in this position of not knowing. That’s essentially where we are with HRV and readiness scores. Perhaps someday this will change and we’ll have good reason to embrace them. NB: I wrote this post. No AI. I have no conflicts of interest with any of its content. Loading... Ground Truths has 215,000 subscribers from every US state and 214 countries. There are over 300,000 followers of Ground Truths so more than 90,000 folks who can easily convert to be free subscribers. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! Share Ground Truths The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you!
Health systems are partnering up without changing who owns the hospital
St. Christopher's, Nemours, Jefferson and Temple signed an alliance letter; Palomar UC San Diego Health went live. Why ownership is not changing hands.
The nation’s premier business group is hitting the road to stamp out socialism
In a six-week trip, the U.S. Chamber of Commerce hopes to convince midterm voters that free markets still offer the best hope for solving the nation’s economic woes.
Efforts to silence 'NAZA' filmmakers, Macklemore only amplifies them
“NAZA” may not need a robust marketing plan if the Israeli government continue to condemn the film. Censorship, and worse, are the surest way to elevate an artist and their work. Just ask Macklemore.
Stellantis Canada plant's sale could allow Chinese entry, mayor warns
"Don't kid yourself, the dominance that the Big Three have had in the Canadian auto market will evaporate," said the mayor of Brampton.
Markets digest fed rate hike, oil prices
The 'Barron's Roundtable' panel analyzes the market impact of the Fed's rate hike and soaring diesel prices.
National Science Foundation awards NMSU grant for chemical simulation
WRITER: Allison Brady, engr_media@nmsu.edu The National Science Foundation awarded nearly half a million dollars to New Mexico State University assistant professor Alejandro Gallegos to build...
Boyer appointed to ABA Emerging Leaders Council
Devin Boyer, senior vice president and Central Iowa market leader for Nicolet National Bank in West Des Moines, has been appointed to the American Bankers Association’s Emerging Leaders Council for the 2026-27 association year. The council works to identify, connect and develop current and future banking leaders and helps guide ABA programming related to leadership…
United Bank hires Phil Poliquin to lead government contracting business
Phil PoliquinUnited Bank, a subsidiary of United Bankshares, Inc. (NASDAQ: UBSI), has announced that Phil Poliquin has joined the Bank as senior vice president and managing director of Government Contracting (GovCon), leading the next phase of growth for United’s GovCon business across the Bank’s footprint.United has a longstanding history of serving government contractors and meeting the dynamic banking needs of companies supporting federal, state and commercial markets. Building on that foundation, the Bank is establishing a dedicated GovCon team led by Poliquin. The new team will bring together bankers from across United's footprint under a focused strategy designed to deepen industry expertise, strengthen client relationships and expand the Bank's presence in this important market.“Government contractors operate in a complex and highly specialized environment, and Phil understands that environment from multiple perspectives,” said United Bank Chief Commercial Lending Officer Mike Proctor in a press release. “His extensive commercial banking experience, combined with more than two decades of military and government service, brings tremendous depth and expertise to our team. We’re excited to have his leadership and insight as we continue strengthening how United serves our GovCon clients across the nation’s capital and beyond.”In his new role, Poliquin will lead United’s GovCon lending strategy and a team of specialists, with a focus on deepening client relationships and expanding the Bank’s presence in the government contracting and defense sectors. He will also serve as a subject matter expert across the organization, partnering with bankers across United’s footprint to support GovCon clients and opportunities.“Government contracting has long been an important part of the economy across the Washington region, and we believe it remains an attractive sector for growth,” said United Chief Executive Officer Richard M. Adams, Jr. in a press release “As the needs of these companies continue to evolve, investing in specialized leadership strengthens our ability to build on our established presence, expand how we serve this market, and pursue new opportunities.”Poliquin has a decade of experience in commercial banking, most recently serving as executive director of Aerospace, Defense, and Government Services (ADG) commercial banking at JPMorgan Chase. In this role, he led a team responsible for providing specialized financing and treasury advice to federal government contractor companies across the nation. Previously, he also held senior leadership positions at Bank of America.Prior to his banking career, Poliquin served 24 years in the U.S. Navy, retiring at the rank of Captain in 2013. A Naval aviator, he flew more than 100 combat missions in operations around the world and served in senior leadership positions, including as commanding officer of an F/A-18 Super Hornet squadron, leading more than 250 personnel during two combat deployments. He later joined the Intelligence Community as a program manager at the National Reconnaissance Office and contributed to the development of the Navy’s unmanned aircraft system strategy.“United has a strong foundation in government contracting, and I’m excited about the opportunity to build on that foundation by expanding our industry presence, deepening relationships, and developing a team with specialized expertise focused on serving government contractors,” said Poliquin in the release. “The goal is to be a trusted resource for growth-oriented companies that need a banking partner who understands what they do, where they’re headed and can help them navigate the opportunities and complexities of this market.”Poliquin holds a bachelor’s degree in mathematics from the University of Oklahoma, a master’s degree in national security strategy from the U.S. Naval War College, and an MBA from the University of North Carolina at Chapel Hill’s Kenan-Flagler Business School.
Joliet refinery shutdown adds fuel to Chicago’s gas-price surge
Chicago drivers are getting another dose of pain at the pump as an extended shutdown of ExxonMobil’s massive Joliet refinery adds pressure to an already tight fuel market.
WA, FTC reach $225M settlement with multilevel marketing company Amway
The lawsuit accused Amway and two of its affiliates of using unfair and deceptive tactics to recruit members.
Minnesota farm-to-table business faces higher costs as fuel prices rise
A farm-to-table business owner says rising fuel prices are negatively impacting her bottom line. Traci Walkowski sells produce and pasture raises turkeys and chickens in Hutchinson, Minnesota and tells Brownfield traveling to farmers markets is getting more expensive. “Obviously we don’t see the impact as much as someone who’s using gallons of diesel to you […]
ArtRio opens amid political uncertainty as Brazil’s art market continues to grow
The Rio fair brings together 76 galleries as an upcoming presidential election and a new generation of collectors shape the market
On Your Side: Missouri attorney general orders prediction markets to stop unlicensed sports wagering
Missouri Attorney General Catherine Hanaway announced that her office has issued cease-and-desist letters to multiple prediction market operators offering sports “event contracts” to Missourians.
Nearly 300,000 Louisianans on Medicaid must soon choose a new plan. Here’s what to know.
People who have the Healthy Blue Medicaid plan in Louisiana will soon have to choose a new insurer after parent company Elevance Health chose to leave the state’s Medicaid market.
Amway, affiliates face $225 million payout over alleged deceptive business practices
The Federal Trade Commission says the settlement is the largest the agency has reached with a multilevel marketing company.
Engrams Embedding Entendre: Codesign for Efficient DRAM/SSD Offloading
Engram extends standard token embeddings with learned multi-token lookups. Recurring local patterns retrieve vectors directly, reducing the need to reconstruct them through attention and feed-forward layers. With Engram model architecture optimization, it allows for lower HBM capacity to be needed for models at the same quality. This does not mean there won’t be an insane demand for HBM but it just means that model architecture will continue to innovate around constraints. This model architecture design is naturally codesigned for parameter offloading: each token accesses a few embedding rows whose addresses depend on token IDs, not hidden states. The runtime can prefetch those rows from host DRAM while earlier layers compute, keeping the table outside HBM without transferring entire weight matrices. Our Memory model contains our latest estimates of quarter by quarter HBM, DRAM, & NAND supply and demand. Offloading frees HBM for model weights and KV cache, potentially supporting larger batches or more concurrent sessions. When DRAM becomes the next constraint, NVMe offers another tier. Recommendation systems already cache frequently or recently accessed embedding rows in faster memory while backing colder rows with SSDs. After NVIDIA roadmap had to change due to massively despec’ing Rubin Ultra from 1024GB to now ~200GB of HBM per chip, model architecture optimizations like emgram maybe helpful. Our DeepSeek-V4.1-Flash configuration uses roughly 189 GiB of memory for Engram. We replace it with a memory-mapped (mmap) file and measure serving performance with offload to SSD. Later on in our report, we will show our Engram offloading experiments along with the official InferenceX agentic inference serving results on Engram models like DeepSeekv4.1 Flash across all 6 NVIDIA GPU SKUs along with MI355X. Unsurprisingly, the CUDA Moat is still mogging MI355X on the ultra popular DeepSeekV4.1 Flash model. We also show how even on high capacity HBM SKUs, offloading emgrams to DRAM could result in even better performance for most of the pareto than keeping the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX is the only inference benchmark in the world to have TPUv7, Jalapeño, Nvidia Rubin NVL72, AMD, and soon, SambaNova and Trainium. Due to how realistic AgentX scenario is to real world agentic inference workloads, AMD has committed to collaborating on MI455X UALoE72 too. DeepSeek did not release the original paper’s two trained Engram models. We replicated its setup on fineweb-edu using the released code and training hyperparameters, at an estimated 6E18 FLOPs per run. We observed the same U-shape scaling: Engram improved performance over pure MoE baselines. We also reproduced DeepSeek’s results, where earlier-layer representations with Engram resembled those of later layers. Like the original Engram paper, it is possible to probe Engram’s gate scores to see what n-grams DeepSeek-V4.1-Flash makes the most use of. Our gate scan finds names, code fragments, relational phrasing, and boilerplate. These examples prioritize interesting-ness over gate strength. One unexpected result was Wright : Ace Attorney. These examples suggest learned memory optimizes the training objective, not a judgment of which facts deserve storage. Licenses, bibliography fragments, API scaffolding, and website furniture can provide prediction shortcuts, so the value of additional Engram capacity may depend on what survives data preparation. This does not show that table capacity is “wasted”: the evaluation-corpus scan establishes neither training exposure nor the capacity occupied by each category. For offloading, strong gates do not identify cache-hot rows. Low gates do not automatically save reads either: computing the gate requires the retrieved key and undoes the performance gain of a fused kernel. Skipping reads would require a separate usefulness predictor before retrieval. In the original paper’s inference-time ablation, factual-knowledge benchmarks retained just 29–44% of their original performance, while reading comprehension retained 81–93%. This is due to the training–inference mismatch. The resulting degradation therefore measures this trained model’s dependence on Engram, not the performance difference between models trained with and without it. In our ablations, suppressing Engram worsens token likelihood across all evaluated domains, especially encyclopedia text and several code corpora. Surprisingly, GSM8K accuracy stays within measured run-to-run variation and removing Engram has no effect. Engram is not a detachable dictionary beside an unchanged MoE. Removing it changes downstream features and expert selection. We tested whether rerouting hurts or compensates by holding tokens fixed in a teacher-forced experiment on CRUXEval, a code-reasoning benchmark of small Python functions where the model predicts a function’s output from its code and an input, and scoring the reference answer. Removing Engram raised answer loss from 0.2848 to 0.3093 bits/token. Forcing the ablated model to use the original Engram-on expert choices made it worse still, at 0.3375 bits/token. Rerouting partially compensates for the missing memory. Memory features and expert selection work together, rather than following a clean “memory stores facts; experts reason” division. On the same CRUXeval, removing Engram during either phase reduced accuracy and increased generated tokens; removing it throughout produced the largest changes. Keeping Engram for prefill leads to more correct answers than keeping for decode likely due to semantically richer KV cache transferred to decode workers, allowing it to mitigate some of the performance loss. Engram’s table is large, but each lookup is small. DeepSeek-V4.1-Flash requests 24 rows at each of two Engram layers, about 12.4 KiB per processed token position across the model, or 3.1 KiB per GPU when split across four GPUs. Currently as of Day 7 since Model Release, MI355X is still 2-4x worse performance per dollar compared to B200 even when normalized by Mi355X’s lower TCO. Our full total cost of ownership breakdown comes from our AI Cloud TCO Model along with monthly market surveys of over 100+ gpu clouds & gpu cloud customers. On the Day 0 release of DeepSeekv4.1 Flash, NVIDIA vLLM works out of the box with zero issues across all 6 SKUs: H100, H200, B200, B300, GB200, GB300! This was thanks to the amazing work by the NVIDIA & Interact teams! In comparison, AMD vLLM did not work on day 0 for DeepSeekv4.1 Flash.Source: SemiAnalysis InferenceX AMD’s vLLM documentation points to using vllm/vllm-openai-rocm:deepseekv41-flash-0909, but from hour 0 of the model release to hour 23, AMD has not publicly released the image. AMD claims, “SPEED IS THE MOAT,” yet it has still not released it by the 23rd hour. We wish that, going forward, the AMD team has a better process for hour 0 model releases. Eventually when they did publicly release for “day 0” image support, performance-wise, it is currently up to 14.8x worse perf per dollar than H200 and up to 42x worse perf per dollar than B200/B300. The power of the CUDA MOAT is NVIDIA’s collaboration with its massive 6 million-developer community ecosystem including most of the vLLM & SGLang & Tokenspeed maintainers which means that CUDA is optimized on day 0. Overall, AMD did make significant improvements but the performance per dollar is still currently 2-4x worse than B200. AgentX Engram DRAM Offloading Improving Performance The HBM and DRAM offload use the same GPU kernel to select and dequantize rows. With HBM, it reads GPU memory; with Unified Virtual Addressing (UVA), it reads pinned host memory directly. Both support full decode graphs. Moving the table into HBM accelerates only the sparse lookup, leaving decoder computation and communication unchanged, resulting in little overall benefit while consuming memory otherwise available to KV cache. Another benefit of Engram offloaded to DRAM which means you can reduce the communication overhead by using less HBM GPUs per replica. For example, when enabling Engram offloading on B300, we are able to switch from TP4 to now TP2 which improves the pareto curve by up to 1.6x. When iso-model quality, less HBM is required as the engrams could be offloaded to host DRAM. Thus HBM bandwidth matters way more than HBM capacity. For inference workloads where memory bandwidth matters the most, 4-hi HBM provides the best $/bandwidth and therefore lowest cost per token. If China continues to make more and more revolutionary model architecture innovations, soon it could potentially 0Hi HBM stacks. [ ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) [ Long Live the Short King: Why 4-hi HBM Wins ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Myron Xie, Bryan Shan, and 3 others · Sep 13 [ Read full story ](https://newsletter.semianalysis.com/p/long-live-the-short-king-why-4-hi) Moreover, on B300 and week-0 stack, moving the Engram table back to HBM did not improve results and stay within run-to-run variance. This is a result of the work optimizing DRAM offload, such as async, and overlap. SSD Offloading On B200, we created a unoptimized vLLM fork and stored the Engram tables in memory-mapped files on local SSDs. File backing lets the OS reclaim table pages when other applications need RAM. Pages already cached in memory can be served without reading the SSD again. Furthermore, note that we were unable to turn on GDS The unoptimized SSD implementation changes how rows reach the GPU. It copies row IDs to the CPU, deduplicates them, gathers the requested rows into pinned buffers, copies those rows back to the GPU and dequantizes them. This work runs between segments of the GPU execution graph. Native UVA performs row selection and dequantization directly on the GPU, avoiding the CPU round trip. A warm filesystem cache removes physical SSD reads, but leaves the coordination, row gathering and transfers. This is why a file already cached in RAM can still perform worse than a pinned DRAM table. The comparison measures the whole serving path; it does not separate the time spent on each of these operations. B200 DRAM dominates both measured SSD serving curves in total tokens per dollar and P90 interactivity. Near 125 tokens/s/user, DRAM delivers 121 million total tokens per dollar versus 52 million for SSD. For production serving, SSD offloading is likely not worth the tradeoff. On the B200 configurations we measured, SSD offloading loses on both measures: every observed SSD point has a DRAM alternative that delivers higher P90 interactivity and more total tokens per dollar. The only points where Cheaper storage does not automatically produce a cheaper inference service. Moving Engram to SSD leaves the same four expensive GPUs and the rest of the server in place. Reclaiming RAM only creates an economic benefit if it enables a cheaper server configuration or additional useful capacity. The current unoptimized path provides neither benefit, and the filesystem cache still consumes RAM when table pages are resident. Next we will look at the mechanisms and specific implementation of ngrams from DeepSeekv4.1 Flash, LongCat, Qwen3.8 Flash Next.
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