I’d like to propose that the worst 1% of appointed judges, as determined by elected bodies, be fired every year. This will weed out the most corrupt and least competent.

Now we speak Get it done @donaldtusk OUT: 🧨 Notaries out, Registry crazy fees out, VAT id months delays and outdated filings out, 0 euros tax filings out, IN: ✅ Right to monitor and ask efficiency data of administrations. Right to monitor indirect efficiencies Cap on time and cost of compliance, tax filings 100% digital, AI-friendly, automation-friendly Making EU-Inc the first AI-friendly, API-enabled company law in the World to let founders automate everything Right to competitiveness, efficiency and right to sue administrations for it. GO GO GO A c c e l e r a t e

human ai interaction vs human human interaction

Stop what you're doing right now and check the sleep data from your wearable to assess your brain’s potential amyloid plaque burden. This is from a new paper that was just published. Instructions: 1. identify the time you fell asleep. 2. mark when you had your first REM sleep cycle. 3. calculate the time it took from falling asleep to your first REM cycle. 4. If <90 min, that's good. 5. If>180 min, that's bad. For example: Asleep by 10 pm. First REM cycle 11:30 pm. That's 90 min. My average this week is: 70 min. REM Latency REML: is the time elapsed from falling asleep and the beginning of the first REM stage. The study found that compared to lowest 25 percentiles of REML (<98.2 min) participants in the top 25 percentiles of REML (>192.7 min) had significantly higher amyloid plaque burden in their brains Study identifies a novel sleep parameter as potential marker for Alzheimer disease. Rem Latency (REML); the delay in REM sleep (dreaming phase) initiation predicts increased amyloid plaque burden in the brain, an increase in dementia marker p-Tau181 and a drop in the brain health marker BNDF in the blood. This parameter has a great potential as a novel risk marker for Alzheimer and dementia since as it can be easily calculated from every wearable. In more detail: Published this week, the study involved 128 participants (64 with Alzheimer's disease, 41 with mild cognitive impairment, and 23 with normal cognition), mean age 70.8, 56.9% female. Participants’ sleep was tracked using polysomnography (gold-standard for sleep monitoring), brain amyloid burden was determined using PET scans, and blood markers p-Tau181 (early prognostic marker for Alzheimer disease), BNDF (Brain health, function, and regeneration marker, drops in Alzheimer disease patients) REM Latency REML: is the time elapsed from falling asleep and the beginning of the first REM stage. The study found that compared to lowest 25 percentiles of REML (<98.2 min) participants in the top 25 percentiles of REML (>192.7 min) had significantly higher amyloid plaque burden in their brains, increased p-Tau181 and diminished BNDF levels, all signifying existing undiagnosed, or future risk of Alzheimer disease. This is a very practical marker that yields itself very useful to self-monitoring using wearable devices, as well as further studying and validation in large remote population studies since it can be calculated using sleep data reported by almost any of the wearables available on market and already used by 100s millions of individuals around the world. My sleep this week shows an average RAML of 70 min which puts me at the ideal spot for this marker.

Technology can be incredible and has made many lives better but EMRs have not yet found the sweet spot. Sad that progress has been so slow on this critical issue.