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Vitamin D & Omega-3 have a larger effect on depression than antidepressants
⏱ This post is over years old.
Proceed at own risk.
The “effect size” of antidepressants on depression, vs placebo, is around 0.4. (On average; some people respond much better or much worse.) This is like going from a C to a C+.
In contrast: the effect size of 1500 mg/day of “≥60% EPA” Omega-3 supplements is a bit higher, around 0.6. This is like going from a C to a B–. (With uncertainty; at worst, Omega-3′s “only” on par with antidepressants.)
But, much better: the effect size of 5000 IU/day of Vitamin D is around 1.8. This is like going from a C to an A–! (With uncertainty; at worst, Vitamin D’s “only” twice as effective as antidepressants.) This works even for people who don’t have a Vitamin D insufficiency, which almost half of American adults do.
Even if you’re already taking Vitamin D & Omega-3, double check your dose: it may still not be enough! The official recommendations are all too low, and recent research suggests even the official maximum safe dose for Vitamin D is too low.
I know the “yay supplements” genre of writing is full of sloppy research & grifters, and you should be skeptical of my claim of easy wins, of “$100 bills laying on the sidewalk”. But there is good science among the trash, and policy is often decades behind science in any field, not just health.
So, Vitamin D & Omega-3: possibly high reward, for low risk. That’s a positive “expected value” bet! These supplements are safe, cheap, over-the-counter, and have positive side-effects (on Covid & cognition). As always, “ask your doctor”, show them the peer-reviewed papers cited in this post.
Unless you have specific reasons to not take Vitamin D & Omega-3 — kidney stones, blood thinners, etc — please try them, for at least a month! They could save your mental health. Maybe even your life.
In Alicetown, the average person has 4 younger cousins.
In Bobtown, the average person has 3 younger cousins.
Alright, not so surprising. You may not even notice a difference.
In Alicetown, the average person has 4 limbs.
In Bobtown, the average person has 3 limbs.
It’s the same absolute difference (4 vs 3) and relative difference (3/4). So what makes limbs more surprising than cousins? Well, partly it’s more dramatic & visible, but also because: we expect high variation in the number of someone’s younger cousins, but not their number of limbs.
This is why scientists calculate an “effect size” or “standardized mean difference” (“mean” = average). We take the difference between two groups, then divide by the total amount of variation, to account for how surprising a difference is.
Unfortunately for laypeople, the effect size is usually just reported as a number, like “+0.74” for spacing out your studying vs cramming, or “–0.776″ for sleep deprivation on attention.
But what’s that mean? How can we make these numbers intuitive?
Well, a common way for data to be is a bell-shaped curve (also called a “normal distribution”). And most of us are, alas, well-acquainted with the bell curve in school grades. (“grading on a curve”)
So: school grades give us a useful way to think about standardized effect sizes! We can now convert that number into an actual letter grade:
For example: spacing out your studying, relative to cramming, will on average lift your test scores from a C to a B–. (effect size = +0.74) And short-term sleep deprivation, relative to healthy sleep, will on average tank your ability to pay attention from a C to a D+. (effect size: –0.776)
But it’s not limited to just grades & academic performance. Effect sizes can also help us understand any kind of difference between groups, in observation or in experiments!
Let’s use our school grade analogy, to interpret effect sizes on mental health:
What’s an “F in mental health”? By definition of a bell curve, ~2.3% of people are below –2 sigma (an “F”). (See: this bell curve calculator.) In Canada, ~2.6% of people had suicidal ideation in 2022, while in the US, it was ~4.9% in 2019. So, it’s not too far off to say: “F in mental health = literally suicidal”. (Also, reminder that ~4% is 1-in-25 people. You likely know someone, or are someone, who will feel suicidal this year. Please reach out to your friends & loved ones!)
What’s a “D in mental health”? ~16% of people are below –1 sigma (a “D”) on a bell curve. The Keyes 2002 study estimated that ~14.1% of adults meet the DSM-III criteria for a major depressive episode. So, D = Depressed.
What’s an average “C in mental health”? ~68% of people are within a sigma of average (a “C”) on a bell curve. Same above study found that 56.6 percent had moderate mental health. They were neither “languishing” nor “flourishing”. I guess C = Could Be Worse.
What’s a “B in mental health”? ~16% of people are above +1 sigma (a “B”) on a bell curve. Same above study found that 17.2% of adults are “flourishing”. Good for them! B = Flourishing, life is good.
What’s an “A in mental health”? I don’t know who these freaks are. I actually could not find any scientific studies on “the +2 sigma in well-being”. In contrast, there’s lots of research on suicidal ideation, the –2 sigma in well-being. In the absence of any actual data, I’ll just say: A = AWESOME
So, if an intervention is found to have an effect size of +1.0, that’s like going up a letter grade. If something’s found to have an effect size of -2.0, that’s like going down two letter grades. And so on.
Okay, so how do we get peoples’ “mental health grades” up?
Let’s look at antidepressants, Omega-3, and Vitamin D, in turn:
The good news is they work. The bad news is they don’t work as well as you’d think they may work.
Cipriani et al 2018 is a meta-analysis: a study that collects & combines lots of previous studies (that pass some basic criteria, to minimize a garbage-in-garbage-out situation). While meta-analyses aren’t perfect, it’s usually better for “science communicators” like me to cite meta-analyses over individual studies, to reduce the chance I’m cherry-picking.
Anyway: this meta-analysis analyzes 522 trials with 116,477 participants. All 21 antidepressants they studied were better than placebo (a pill that contains no active medicine). The most effective antidepressant, Amitriptyline, had an “Odds Ratio” of 2.13, which converts to an effect size of 0.417, which is “small-medium” according to Cohen’s recommendations. Or, by our school-letter-grade comparison: the best antidepressant would take your mental health grade from an F to F+, or C to C+.
From Figure 3 of that paper, you can see that Amitriptyline has the highest estimated effect size, while the side effects are no worse than placebo:
But hang on, only F to F+ on average? How does that square with people’s personal experience that antidepressants have been lifesaving?
Well, first: the average person has around 1 testicle.
The punchline being ~50% of people have 2 testicles while ~50% of people have 0 testicles, hence the average is “around 1”. Likewise, the average effect for the best antidepressant is 0.4 — but some people respond much better to that… and some respond much worse.
And, second: the belief that things will get better is a powerful thing. Unfortunately, the power of hope gets a bad name in medicine: “placebo”.
When you take any medicine, you don’t just get (effect of medicine). You get (effect of medicine + effect of placebo + effect of time).
And what is the effect of placebo? Amazingly, despite researchers having used placebos for decades, it’s only recently that we started testing “open-label” placebos: placebos where we just tell the patient it’s a placebo. We then compare “getting placebo” to “getting nothing”. The effect size of open placebo, on stuff ranging from pain to depression, is around 0.43. (Spille et al 2023)
That is: the effect of (placebo vs nothing) is as strong as (the best antidepressant vs placebo)! But again, I think “placebo” is an insulting word for the power of hope. Hope isn’t magic, but it’s measurably not-nothing. I assert: we shouldn’t dismiss such a connection between mental state & physical health.
But anyway, for the rest of this article, I’ll only be reporting effect sizes versus placebo. Just remember that the power of hope gives you an extra +0.4 (like C to C+) for all interventions.
Keep getting confused on which fat is what? Me too. So, here’s a crash course on various fats:
Fatty acids are chains of carbons & hydrogens + two oxygens. They say “OOH” at one end, and “HHH” at the other end:
A saturated fatty acid is one where all the carbons’ free spots are filled up with hydrogens. (Hence, “saturated”) This makes the molecule stick straight out. This is why long saturated fatty acids — like those found in butter — tend to be solid at room temperature.
In contrast, unsaturated fatty acids have at least one hydrogen missing. This causes them to have a double-bond “kink” in the molecule. This makes them not stick out, which is why unsaturated fats tend to be liquid at room temperature. Mono-unsaturated fatty acids (MUFAs) — like in olive oil — only have one kink. Poly-unsaturated fatty acids (PUFAs) — like in fatty fish — have two or more kinks. Let’s be mature adults about this, please.
For completeness: trans fats are unsaturated fats whose “kink” is twisted around, causing them to go straight. That is the worst sentence I’ve written all month. The twisted kink is caused by the hydrogens being on opposite sides, hence “trans”. (And yes, if they’re on the same side it’s “cis”. Latin was a mistake.) The molecule being straight is why trans fats — which margarine used to be full of — are solid at room temperature, despite being an unsaturated fat.
It’s neat whenever you can trace the history of something right down to its atoms! Margarine was first invented because it’s cheaper, and is spreadable straight from the fridge, unlike butter. Margarine (used to be) made by taking unsaturated vegetable oils, which were cheaper than animal fats, then pumping a bunch of hydrogens into it (hence, “hydrogenated oils”). If you completely hydrogenate an oil, it becomes a saturated fat. But they only partially hydrogenated those oils, leading to trans fats, which were cheaper & a spreadable semi-solid at fridge temperature.
In the 1970s & 80s, the US Food & Drug Administration concluded that trans fats were not harmful to humans, and nutritionists promoted margarine over butter, because butter had “unhealthy” saturated fats. But in the early 1990s, scientists realized that trans fats were even worse for you than saturated fats. Only in the 2010′s, did most Western countries start officially banning trans fats. Reminder: policy is often decades behind science.
I need to stop going on infodump tangents. Anyway, Omega-3 is any fatty acid with its first kink at the 3rd carbon from the Omega end (“HHH”), though it can have more kinks later down the chain. (And yes, Omega-6 has its first kink at the 6th carbon, and Omega-9 has its first kink at the 9th carbon. There’s nothing physically preventing Omega-4 or Omega-5′s from existing, but due to some quirk of evolution, Omega-3, -6, and -9 are the ones biological life uses most. As far as I can tell, there’s no specific reason they’re all multiples of 3. Probably just a coincidence. There is a less common Omega-7.)
Finally, there’s three main types of Omega-3: EPA (Eicosapentaenoic Acid), DHA (Docosahexaenoic Acid), and ALA (Alpha-Linolenic Acid). ALA is mostly found in plants like chia seeds & walnuts, while EPA & DHA mostly come from seafood, though there are algae-based vegan sources.
EPA & DHA are the focus of this section. For bio-mechanical reasons I don’t understand but I assume someone else does: EPA is the one associated with anti-inflammation, better brain health, and less depression… while DHA isn’t. (But DHA is still needed for other stuff, like your neurons’ cell walls, so don’t cut them out completely!)
All the above info in a Venn (technically Euler) diagram:
Okay, enough yap. Time for the actual data:
Sublette et al 2011 is an older meta-analysis (15 trials with 916 participants). It’s the only meta-analysis I could find that estimates the actual “dose-response” curve, which shows: how much effect, for how much treatment.
Why is dose-response important? Because one problem with many meta-analyses is they’ll do something like: “Study 1 gave patients 1 gram of medicine and saw a +1 improvement in disease, Study 2 gave 10 grams and saw +4 improvement, Study 3 gave 100 grams and saw negative –5 improvement… the average of +1, +4, and –5 is zero… therefore the medicine’s effect is zero.”
As mentioned earlier, this is a meaningless mean. That’s why we want to know the response at each dose.
Anyway, the Sublette meta-analysis gathered randomized trials studying Omega-3 on depression (vs placebo, of course) and got the following dose-response curve.⤵ Note that the horizontal axis is not just amount of total Omega-3, but specifically the extra amount of “unopposed” EPA, above the amount of DHA. Or in other words, “EPA minus DHA”:
The top effect size is around +0.558, which is like going from an F to D–, or C to B–. You get this maximum effect around 1 to 2 grams of extra EPA, and too much EPA gets worse results. The meta-analysis finds that Omega-3 supplements that are ~60% EPA (and the rest DHA) are optimal.
This finding is roughly in line with later meta-analyses. Liao et al 2019 also finds that ~1 gram of ≥60% EPA is best, but actually found a much higher effect size: +1.03. Kelaiditis et al 2023 also finds 1 to 2g of ≥60% EPA is best, but found a lower effect size of +0.43… which is still as good as the best antidepressant!
Either way, let’s boil this down to a recommendation. You want around 1 gram of EPA a day. So if your supplements are 60% EPA, you need 1 gram ÷ 0.6 ~= 1.667 grams = 1667 milligrams. Let’s round this down for convenience: get 1500 mg/day of 60%-EPA Omega-3 supplements.
In comparison, most official health organizations recommend “250–500 mg combined EPA and DHA each day for healthy adults.” That is over three times too low, at least for optimal effects on depression. Which, as we calculated above, is probably around 1500 mg/day. (The official safe dose is 5000 mg/day)
Finally, a (small) study directly investigating the link between suicide & Omega-3. Sublette et al 2006: “Low [DHA] and low Omega-3 proportions […] predicted risk of suicidal behavior among depressed patients over the 2-year period.” Though keep in mind this is a small study, and it’s observational not experimental. Also, weird that contrary to the above studies on depression, DHA predicted suicide but not EPA. Not sure what to make of that.
Bonus: Omega-3 may also boost cognition? Shahinfar et al 2025: “Enhancement of global cognitive abilities was observed with increasing omega-3 dosage up to 1500 mg/day. [effect size = 1.00, like going from a grade of C to B!], followed by downward trend at higher doses.”
Ghaemi et al 2024 is a meta-analysis on Vitamin D on depression (31 trials with 24,189 participants).
Again, it actually estimates a dose-response curve! Below is Figure 1 + Table 2, showing the effect of Vitamin D dosage on depression vs placebo. The solid line is the average estimated effect, dashed lines are 95% confidence interval. Note the effect size is negative in this figure, because they’re measuring reduction in depressive symptoms:
The upper range of uncertainty is lowest at 5000 IU (International Units) of Vitamin D a day, with an estimated effect size of 1.82, with a 95% uncertainty range, from 0.98 to 2.66. An effect size of 1.82 is like taking your mental health from an F to a C–, or a C to an A–! And even in the most pessimistic case, 0.98, that’s still over twice as effective as the top antidepressant!
* The paper’s summary says 8000 IU is best, with effect size 2.04, but there’s much greater uncertainty there.
* The paper also finds that longer studies had smaller effects than shorter studies, but this does not necessarily mean Vitamin D’s effects are short-lived. Looking at Supplementary Table 4, it seems this is partly because longer studies used lower average daily doses. (For example, one 52-week study only gave participants 400 IU a day.)
* Other meta-analyses report lower effects, because they use “meaningless means”. If you have nine trials at 400 IU/day with effect +0.5, and one trial at 4000 IU/day with effect +1.5, your “average” effect is +0.6. Again, that’s why I chose this meta-analysis: it estimates the actual dose-response curve.
* Table 1 also shows that Vitamin D helps for both patients using antidepressant medication, and not. This is encouraging: it means you can stack both medications & supplements!
* Admittedly, Table 1 seems to imply that Vitamin D supplementation didn’t help participants without Vitamin D deficiency, but:
You probably are lacking Vitamin D: Liu et al 2018 finds that a bit under half of all adults (41.4%) have Vitamin D Insufficiency.
Looking at Supplementary Table 4, the trials whose participants didn’t have Vitamin D deficiency (understandably) used much lower doses. That’s probably why they had a much lower effect.
* You probably are lacking Vitamin D: Liu et al 2018 finds that a bit under half of all adults (41.4%) have Vitamin D Insufficiency.
* Looking at Supplementary Table 4, the trials whose participants didn’t have Vitamin D deficiency (understandably) used much lower doses. That’s probably why they had a much lower effect.
The “official” recommendations are all too low:
So, if this meta-analysis is right, then 5000 IU/day is around optimal. But the official recommendation for Vitamin D is 400–800 IU/day, over six times too low. 5000 IU/day is even higher than the “official maximum safe dose”, of 4000 IU/day! But McCullough et al 2019 gave over thousands of patients 5,000 to 10,000 IU/day, for seven years, and there were zero cases of serious side effects. This matches later studies like Billington et al 2020, a 3-year-long double-blinded randomized trial, which found “the safety profile of vitamin D supplementation is similar for doses of 400, 4000, and 10,000 IU/day.” (Although 15 participants got “mild hypercalcemia”, but “all cases resolved on repeat testing.” Either way, that’s a small cost for reducing the risk of major depression & suicide.)
And it makes evolutionary sense that 10,000 IU a day should be safe. Your skin, exposed to the Sun’s ultraviolet rays, can synthesize up to (the equivalent of) 10,000 IU a day, before plateauing out. Source is Vieth 1999: “Because vitamin D is potentially toxic, intake of [1000 IU/day] has been avoided even though the weight of evidence shows that the currently accepted [limit] of [2000 IU/day] is too low by at least 5-fold.”
Speaking of the Sun, why take supplements instead of just getting Vitamin D from sun exposure? Well, skin cancer. But also: because Sun-Skin D varies greatly depending on the season, your latitude, and your skin type. There’s less ultraviolet rays from the Sun in winter/fall, and at latitudes further from the equator. And the darker your skin is, the less Vitamin D your skin makes for the same amount of Sun exposure. As expected from the bio-physics of skin, Black adults have the highest prevalence of Vitamin D deficiency (82.1%!!), followed by Hispanic adults (62.9%). (But hey, at least Black adults have the lowest incidence of skin cancer. You win some you lose some.) The point is: speaking as someone with Southeast Asian skin, who’s currently in Canada during winter… even if I stood outside naked for hours, I’d get approximately zero IU/day of Vitamin D from the Sun. Thus: supplements.
Direct effect on suicide: Finally, a meta-analysis directly measuring the effect of Vitamin D on suicidal behaviour. Yu et al 2025: “Vitamin D in patients with [suicidal behaviours] were significantly lower than in controls (standardized mean difference: –0.69, or a ‘medium’ difference)”. Reminder that this paper by itself only measures correlation, not causation — but combined with the above experiments of Vitamin D on depression, I think it’s reasonable to guess it’s partly causal.
* Almost half of you have a Vitamin D deficiency according to the official recommendation (800 IU/day).
* And the official recommendation is way too low. Even the official maximum safe dose (4000 IU/day) is below the optimal Vitamin D for depression (5000 IU/day) or what your body can produce from the Sun in optimal conditions (10,000 IU/day). Recent randomized controlled trials confirm that 10,000 IU/day is, indeed, mostly safe.
* And even if you do 3000 IU/day, well below the max safe limit, the expected effect is still better than the best antidepressant, even on the pessimistic end of the estimate!
* Reminder that official policy is often decades behind the science.
* Reminder that I’m not saying “take supplements instead of antidepressants”; in fact the above meta-analysis shows you can effectively stack them!
Bonus: Vitamin D supplementation was found in several randomized controlled trials to reduce mortality from Covid-19, though much less than official treatments like Paxlovid. Vitamin D also probably helps guard against influenza too, though the evidence is small & early.
Scurvy is caused by a lack of Vitamin C. It’s a condition that causes your wounds to re-open up & teeth to fall out. Scurvy used to kill almost half(!) of all sailors on major expeditions; it’s estimated millions died. It can be cured by eating lemons.
Rickets is mostly caused by a lack of Vitamin D. It’s a condition where kids’ bones go all soft and deformed. During the Industrial Revolution, up to 80% of kids suffered from it. It can be prevented with cod liver oil.
Goiters is mostly caused by a lack of Iodine. It’s a condition where the thyroid gland in your neck swells up painfully, to the size of an apple. During WWI, a third of adult men had goiters. It can be prevented with iodized salt.
About 1 in 4 people are expected to have clinical depression sometime in their life. Depression is the #1 source of the global “burden from disease” in the mental health category, and that category is the #6 burden of disease in the world, above Alzheimer’s, malaria, and sexually transmitted infections.
The effective altruists are all, “woah for just $3000 you can prevent a child’s death from malaria” — and that’s great! save them kids! — but where’s the fanfare for the accumulating evidence that, “woah with cheap daily supplements we can save millions from suicide & depressed lives”?
Over and over again throughout history, some horrific thing that caused millions to suffer, turned out to be “yeah you were missing this one molecule lol”. To be clear: not everything is gonna be that simple, and mental health is not “just” chemistry. Also, all the numbers on this page have with large error bars & uncertainty, more research is needed.
But, as of right now, I feel I can at least confidently claim the following:
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“[Nvidia CEO] Jensen Huang Is Begging You to Stop Being So Negative About AI” — Headline from Gizmodo
Guys, enough is enough. Bullying is a serious issue, and it’s time for me to speak out. There’s an extremely hurtful narrative going around that my product, a revolutionary new technology that exists to scam the elderly and make you distrust anything you see online, is harmful to society. This slander is totally unwarranted, and I would really appreciate it if everyone would stop being so mean about this thing I just invested a billion dollars in.
As someone who desperately needs this technology to work out, I can honestly say it is the most essential tool ever created in all of human history. Don’t mercilessly ridicule it just because it steals the joy out of your hobbies and creates sexually explicit images of women without their consent. Seriously, please stop! It really hurts my feelings.
It’s easy to throw stones if you think about the job displacement and ecological destruction caused by this pointless technology. But such black-and-white, not-wanting-billionaires-to-get-richer thinking is, quite frankly, cruel. You can’t just measure the value of something in terms of “whether or not it makes everything worse for everyone.” The world is much more complicated than that.
This technology is going to fuel innovation across industries and solve all problems of feminism and equal rights. Yes, it’s expanding the surveillance state, and yes, it’s destroying the education system, and yes, it’s being trained on copyrighted work without permission, and yes, it’s being used to create lethal autonomous weapons systems that can identify, target, and kill without human input, but… I forget my point, but ultimately, I think you should embrace it.
Lately, I feel like I just can’t win with you guys. Please, just use my evil technology. What’s so wrong with that? Just use it. I’m begging you. I want to continue living my immoral technofascist life without any criticism.
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The first images from the Meteosat Third Generation-Sounder satellite have been shared at the European Space Conference in Brussels, showing how the mission will provide data on temperature and humidity, for more accurate weather forecasting over Europe and northern Africa.
The images from Meteosat Third Generation-Sounder (MTG-S) show a full-disc image of Earth as seen from geostationary orbit, about 36 000 km above Earth’s surface. These images were captured on 15 November 2025 by the satellite’s Infrared Sounder instrument. In the ‘temperature’ image (below), the Infrared Sounder used a long-wave infrared channel, which measured Earth’s surface temperature as well as the temperature at the top of clouds. Dark red corresponds to high temperatures, mainly on the warmer land surfaces, while blue corresponds to lower temperatures, typically on the top of clouds.As would be expected, most of the warmest (dark red) areas in this image are on the continents of Africa and South America. In the top-centre of the image, the outline of the coast of western Africa is clearly visible in dark red, with the Cape Verde peninsula, home to Senegal’s capital Dakar, visible as among the warmest areas in this image. In the bottom-right of the image, the western coast of Namibia and South Africa are also visible in red beneath a swirl of cold cloud shown in blue, while the northeast coast of Brazil is visible in dark red on the left of the image.
The ‘humidity’ image (below) was captured using the Infrared Sounder’s medium-wave infrared channel, which measures humidity in Earth’s atmosphere. Blue colours correspond to regions in the atmosphere with higher humidity, while red colours correspond to lower humidity in the atmosphere.The outlines of landmasses are not visible in this image. The areas of least atmospheric humidity, shown in dark red, are seen approximately over the Sahara Desert and the Middle East (top of image), while a large area of ‘dry’ atmosphere also covers part of the South Atlantic Ocean (centre of image). Numerous patches of high humidity are seen in dark blue over the eastern part of the African continent as well as in high and low latitudes.
Below we see a close-up from MTG-Sounder of the European continent and part of northern Africa. Like the first image above, here we see heat from land surfaces and temperatures at the top of clouds. The heat from the African continent is seen in red in the lower part of the image, while a dark blue weather front covers Spain and Portugal. The Italian peninsula is in the centre of the image.
Temperatures over Europe and northern Africa by MTG-Sounder
And the animation (below) uses data from the MTG-Sounder satellite to track the eruption of Ethiopia’s Hayli Gubbi volcano on 23 November 2025. The background imagery shows surface temperature changes while infrared channels highlight the developing ash plume. The satellite’s timely observations enable tracking of the evolving ash plume over time.
MTG is a world-class Earth observation mission developed by the European Space Agency (ESA) with European partners to address scientific and societal challenges. The mission provides game-changing data for forecasting weather and air quality over Europe.The satellite’s geostationary position above the equator means it maintains a fixed position relative to Earth, following the same area on the planet’s surface as we rotate. This enables it to provide coverage of Europe and part of northern Africa on a 15-minute repeat cycle. It supplies new data on temperature and humidity over Europe every 30 minutes, supplying meteorologists with a complete weather picture of the region and complementing data on cloud formation and lightning from the MTG-Imager (MTG-I) satellite.
ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said, “Seeing the first Infrared Sounder images from the MTG-Sounder satellite really brings this mission and its potential to life. We expect data from this mission to change the way we forecast severe storms over Europe — and this is very exciting for communities and citizens, as well as for meteorologists and climatologists. As ever, the outstanding work done by our teams in collaboration with long-standing partners, including Eumetsat, the European Commission and dozens of European industry teams, means we now have the ability to predict extreme weather events in more accurate and timely ways than ever before.”The Infrared Sounder instrument on board MTG-S is the first European hyperspectral sounding instrument in geostationary orbit. It is designed to generate a completely new type of data product. It uses interferometric techniques, which analyse miniscule patterns in light waves, to capture data on temperature and humidity, as well as being able to measure wind and trace gases in the atmosphere. The data will eventually be used to generate three-dimensional maps of the atmosphere, helping to improve the accuracy of weather forecasting, especially for nowcasting rapidly evolving storms.“It’s fantastic to see the first images from this groundbreaking mission,” said James Champion, ESA’s MTG Project Manager. “This satellite has been 15 years in development and will revolutionise weather forecasting and especially nowcasting. The ability to vertically profile the full Earth’s disk with a repeat cycle of only 30 minutes for Europe is an incredible accomplishment!”
“I’m excited that we can share these first images from the Infrared Sounder, which showcase just a small selection of the 1700 infrared channels continuously acquired by the instrument as it observes Earth,” said Pieter Van den Braembussche, MTG System and Payload Manager at ESA. “By combining all 1700 channels, we will soon be able to generate three dimensional maps of temperature, humidity and even trace gases in the atmosphere. This capability will offer a completely new perspective on Earth’s atmosphere, not previously available in Europe, and is expected to help forecasters predict severe storms earlier than is possible today.”
The MTG mission currently has two satellites in orbit: MTG-I and MTG-S. The second Imager will be launched later in 2026.MTG-S was launched on 1 July 2025. Thales Alenia Space is the prime contractor for the overall MTG mission, with OHB Systems responsible for the MTG-Sounder satellite. Mission control and data distribution are managed by Eumetsat.The MTG-S satellite also hosts the Copernicus Sentinel-4 mission, which consists of an ultraviolet, visible and near-infrared (UVN) imaging spectrometer. Sentinel-4 delivered its first images last year.
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Tesla CEO Elon Musk said on Wednesday that the automaker is ending production of its Model S and X vehicles, and will use the factory in Fremont, California, to build Optimus humanoid robots.
“It’s time to basically bring the Model S and X programs to an end with an honorable discharge,” Musk said on the company’s fourth-quarter earnings call. “If you’re interested in buying a Model S and X, now would be the time to order it.”
After the original Roadster, the two models are Tesla’s oldest vehicles, and in recent years the company has slashed prices as global competition for electric vehicles has soared. Tesla started selling the Model S sedan in 2012, and the Model X SUV three years later.
On Tesla’s website, the Model S currently starts at about $95,000, while the Model X starts at around $100,000
Tesla’s far more popular models are the 3 and Y, which accounted for 97% of the company’s 1.59 million deliveries last year. The Model 3 now starts at about $37,000, and the Model Y is around $40,000. Tesla debuted more affordable versions of the vehicles late last year.
In its earnings announcement on Wednesday, Tesla reported its first annual revenue decline on record, with sales falling in three of the past four quarters. Musk has been trying to turn attention away from traditional EVs and toward a future of driverless cars and humanoid robots, areas where the company currently has virtually no business.
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The goal of this tracker is to detect statistically significant degradations in Claude Code with Opus 4.5 performance on SWE tasks. What you see is what you get: We benchmark in Claude Code CLI with the SOTA model (currently Opus 4.5) directly, no custom harnesses.
Shows if any time period has a statistically significant performance drop (p < 0.05).
Historical average pass rate used as reference for detecting performance changes.
Percentage of benchmark tasks passed in the most recent day’s evaluations.
Aggregate pass rate over the last 7 days. Provides a more stable measure than daily results.
Aggregate pass rate over the last 30 days. Best measure of overall sustained performance.
Daily benchmark pass rates over the past 30 days. Hover over legend items for details on each visual element.
Daily benchmark pass rate showing the percentage of tasks solved each day.
Historical average pass rate (58%) used as reference for detecting performance changes.
Shaded region around baseline (±14.0%). Changes within this band are not statistically significant (p ≥ 0.05).
95% confidence interval for each data point. Toggle checkbox to show/hide. Wider intervals indicate more uncertainty (fewer samples).
Historical average pass rate (58%) used as reference for detecting performance changes.
Shaded region around baseline (±5.6%). Changes within this band are not statistically significant (p ≥ 0.05).
95% confidence interval for each data point. Toggle checkbox to show/hide. Wider intervals indicate more uncertainty (fewer samples).
The goal of this tracker is to detect statistically significant degradations in Claude Code with Opus 4.5 performance on SWE tasks. We are an independent third party with no affiliation to frontier model providers.
Context: In September 2025, Anthropic published a
postmortem on Claude degradations. We want to offer a resource to detect such degradations in the future.
We run a daily evaluation of Claude Code CLI on a curated, contamination-resistant subset of
SWE-Bench-Pro. We always use the latest available Claude Code release and the SOTA model (currently Opus 4.5). Benchmarks run directly in Claude Code without custom harnesses, so results reflect what actual users can expect. This allows us to detect degradation related to both model changes and harness changes.
Each daily evaluation runs on N=50 test instances, so daily variability is expected. Weekly and monthly results are aggregated for more reliable estimates.
We model tests as Bernoulli random variables and compute 95% confidence intervals around daily, weekly, and monthly pass rates. Statistically significant differences in any of those time horizons are reported.
Get notified when degradation is detected We’ll email you when we detect a statistically significant performance drop. Thanks for subscribing! Check your email to confirm. Something went wrong. Please try again.
...
Read the original on marginlab.ai »
The UK Government recently unveiled its ‘AI Skills Hub’, which wants to provide 10 million workers with AI skills by 2030. The main site was delivered by PwC for the low, low price of.. £4.1 million (~$5,657,000).
It is not good. Like, at all - the UI is insanely bad and it’s clear that this was just a vibecoded site (to be fair, this is the AI Skills Hub, but c’mon, where is the pride in your work? I would be ashamed to even release this as a prototype!)
PwC didn’t even write any of the course content! The only thing the Skills Hub does is link out to external pages, like Salesforce’s free Trailhead learning platform:
Note that I’m fairly certain this course already existed before the contract was even awarded, so all the site does is.. link out to other sites?
PwC itself also admits that the site does not properly meet accessibility standards:
Even for those without a disability, the lack of here in this regard means that the site can be very confusing and buggy as a result.
The site has a course on “AI and intellectual property”. One thing it mentions is fair use:
Except that fair use is not a thing in the UK - that’s a US concept! The UK uses what’s known as “fair dealing”, which is more restrictive than fair use, so the details here are plain wrong.
The interface for this website has also not been clearly thought out - one glaring example is the process of actually enrolling in a course.
On the course page, the “Enroll Now” button is tiny, and if you don’t see it and try scrolling down to the bottom, you will find yourself nothing but a comment section!
Then you have other bugs too, like the “Skills & Training Gap Analysis” - which is linked at the top of the site! - apparently being closed off to the public for no reason:
To be honest, seeing this made me angry.
I’m angry at the sheer wastefulness of the UK Government here. Our public services are collapsing - while £4 million is admittedly chump change for the UK government, there are real people behind these numbers - families waiting months for NHS appointments, children in crumbling schools, vulnerable people not getting the care they need. The waste feels particularly galling when you realise that almost no one will actually use this site!
I’m also angry that the small webdev businesses we have here in the UK were left out of this - for less than 5% of the cost, we’d have a better website and help out small businesses who actually care about their work, instead of handing the project to a multinational company that made nearly $60 billion in revenue in a year and has zero qualms about ripping off the British taxpayer.
A reader sent me another “AI Delivery” site from the government, that was also AI generated. This one is admittedly a fair bit better — for one thing, we didn’t pay £4.1 million for it! — but have some pride in your work, for goodness sake!
...
Read the original on mahadk.com »
Diagrams are essential for AI-assisted programming. When you’re working with an AI coding assistant, being able to visualize data flows, state machines, and system architecture—directly in your terminal or chat interface—makes complex concepts instantly graspable.
Mermaid is the de facto standard for text-based diagrams. It’s brilliant. But the default renderer has problems:
* Aesthetics — Might be personal preference, but wished they looked more professional
* No terminal output — Can’t render to ASCII for CLI tools
* Heavy dependencies — Pulls in a lot of code for simple diagrams
We built beautiful-mermaid at Craft to power diagrams in Craft Agents. It’s fast, beautiful, and works everywhere—from rich UIs to plain terminals.
The ASCII rendering engine is based on mermaid-ascii by Alexander Grooff. We ported it from Go to TypeScript and extended it Thank you Alexander for the excellent foundation! (And inspiration that this was possible.)
* 15 built-in themes — And dead simple to add your own
* Full Shiki compatibility — Use any VS Code theme directly
npm install beautiful-mermaid
# or
bun add beautiful-mermaid
# or
pnpm add beautiful-mermaid
import { renderMermaid } from ‘beautiful-mermaid’
const svg = await renderMermaid(`
graph TD
A[Start] –> B{Decision}
B –>|Yes| C[Action]
B –>|No| D[End]
import { renderMermaidAscii } from ‘beautiful-mermaid’
const ascii = renderMermaidAscii(`graph LR; A –> B –> C`)
Also available via jsDelivr. The bundle exposes renderMermaid, renderMermaidAscii, THEMES, DEFAULTS, and fromShikiTheme on the global beautifulMermaid object.
The theming system is the heart of beautiful-mermaid. It’s designed to be both powerful and dead simple.
Every diagram needs just two colors: background (bg) and foreground (fg). That’s it. From these two colors, the entire diagram is derived using color-mix():
const svg = await renderMermaid(diagram, {
bg: ‘#1a1b26’, // Background
fg: ‘#a9b1d6’, // Foreground
This is Mono Mode—a coherent, beautiful diagram from just two colors. The system automatically derives:
For richer themes, you can provide optional “enrichment” colors that override specific derivations:
const svg = await renderMermaid(diagram, {
bg: ‘#1a1b26’,
fg: ‘#a9b1d6’,
// Optional enrichment:
line: ‘#3d59a1’, // Edge/connector color
accent: ‘#7aa2f7’, // Arrow heads, highlights
muted: ‘#565f89’, // Secondary text, labels
surface: ‘#292e42’, // Node fill tint
border: ‘#3d59a1’, // Node stroke
If an enrichment color isn’t provided, it falls back to the color-mix() derivation. This means you can provide just the colors you care about.
All colors are CSS custom properties on the element. This means you can switch themes instantly without re-rendering:
// Switch theme by updating CSS variables
svg.style.setProperty(‘–bg’, ‘#282a36’)
svg.style.setProperty(‘–fg’, ‘#f8f8f2’)
// The entire diagram updates immediately
15 carefully curated themes ship out of the box:
import { renderMermaid, THEMES } from ‘beautiful-mermaid’
const svg = await renderMermaid(diagram, THEMES[‘tokyo-night’])
Creating a theme is trivial. At minimum, just provide bg and fg:
const myTheme = {
bg: ‘#0f0f0f’,
fg: ‘#e0e0e0’,
const svg = await renderMermaid(diagram, myTheme)
Want richer colors? Add any of the optional enrichments:
const myRichTheme = {
bg: ‘#0f0f0f’,
fg: ‘#e0e0e0’,
accent: ‘#ff6b6b’, // Pop of color for arrows
muted: ‘#666666’, // Subdued labels
Use any VS Code theme directly via Shiki integration. This gives you access to hundreds of community themes:
import { getSingletonHighlighter } from ‘shiki’
import { renderMermaid, fromShikiTheme } from ‘beautiful-mermaid’
// Load any theme from Shiki’s registry
const highlighter = await getSingletonHighlighter({
themes: [‘vitesse-dark’, ‘rose-pine’, ‘material-theme-darker’]
// Extract diagram colors from the theme
const colors = fromShikiTheme(highlighter.getTheme(‘vitesse-dark’))
const svg = await renderMermaid(diagram, colors)
The fromShikiTheme() function intelligently maps VS Code editor colors to diagram roles:
For terminal environments, CLI tools, or anywhere you need plain text, render to ASCII or Unicode box-drawing characters:
import { renderMermaidAscii } from ‘beautiful-mermaid’
// Unicode mode (default) — prettier box drawing
const unicode = renderMermaidAscii(`graph LR; A –> B`)
// Pure ASCII mode — maximum compatibility
const ascii = renderMermaidAscii(`graph LR; A –> B`, { useAscii: true })
renderMermaidAscii(diagram, {
useAscii: false, // true = ASCII, false = Unicode (default)
paddingX: 5, // Horizontal spacing between nodes
paddingY: 5, // Vertical spacing between nodes
boxBorderPadding: 1, // Padding inside node boxes
The ASCII rendering engine is based on mermaid-ascii by Alexander Grooff. We ported it from Go to TypeScript and extended it with:
Thank you Alexander for the excellent foundation!
...
Read the original on github.com »
The acting head of the US government’s top cybersecurity agency reportedly uploaded sensitive government files into a public version of ChatGPT, triggering internal security alerts and a federal review.
A Politico investigation claims Madhu Gottumukkala, the interim director of the Cybersecurity and Infrastructure Security Agency, uploaded contracting documents marked “For Official Use Only” into ChatGPT last summer.
The report says Gottumukkala requested a special exemption to access ChatGPT, which is blocked for other Department of Homeland Security staff.
Cybersecurity monitoring systems then reportedly flagged the uploads in early August. That triggered a DHS-led damage assessment to determine whether the information had been exposed.
Public versions of ChatGPT share user inputs with OpenAI, which raised concerns inside the federal government about sensitive data leaving internal networks.
CISA spokesperson Marci McCarthy told Politico that Gottumukkala “was granted permission to use ChatGPT with DHS controls in place,” adding that the use was “short-term and limited.”
Gottumukkala has served as acting director since May, while the Senate has yet to confirm Sean Plankey as permanent head of the agency.
The ChatGPT incident follows other reported issues during Gottumukkala’s tenure. Politico said he previously failed a counterintelligence polygraph required for access to highly sensitive intelligence. During congressional testimony last week, he rejected that characterization when questioned.
The report lands as the administration of US President Donald Trump continues to push AI adoption across federal agencies.
Trump signed an executive order in December aimed at limiting state-level AI regulation, while the Pentagon has announced an “AI-first” strategy to expand the military’s use of artificial intelligence.
...
Read the original on www.dexerto.com »
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This content is generated by Google AI. Generative AI is experimental
In August, we previewed Genie 3, a general-purpose world model capable of generating diverse, interactive environments. Even in this early form, trusted testers were able to create an impressive range of fascinating worlds and experiences, and uncovered entirely new ways to use it. The next step is to broaden access through a dedicated, interactive prototype focused on immersive world creation. Starting today, we’re rolling out access to Project Genie for Google AI Ultra subscribers in the U.S (18+). This experimental research prototype lets users create, explore and remix their own interactive worlds.A world model simulates the dynamics of an environment, predicting how they evolve and how actions affect them. While Google DeepMind has a history of agents for specific environments like Chess or Go, building AGI requires systems that navigate the diversity of the real world.To meet this challenge and support our AGI mission, we developed Genie 3. Unlike explorable experiences in static 3D snapshots, Genie 3 generates the path ahead in real time as you move and interact with the world. It simulates physics and interactions for dynamic worlds, while its breakthrough consistency enables the simulation of any real-world scenario — from robotics and modelling animation and fiction, to exploring locations and historical settings.Building on our model research with trusted testers from across industries and domains, we are taking the next step with an experimental research prototype: Project Genie.Project Genie is a prototype web app powered by Genie 3, Nano Banana Pro and Gemini, which allows users to experiment with the immersive experiences of our world model firsthand. The experience is centred on three core capabilities:
Prompt with text and generated or uploaded images to create a living, expanding environment. Create your character, your world, and define how you want to explore it — from walking to riding, flying to driving, and anything beyond.For more precise control, we have integrated “World Sketching” with Nano Banana Pro. This allows you to preview what your world will look like and modify your image to fine tune your world prior to jumping in. You can also define your perspective for the character — such as first-person or third-person — giving you control over how you experience the scene before you enter.
Your world is a navigable environment that’s waiting to be explored. As you move, Project Genie generates the path ahead in real time based on the actions you take. You can also adjust the camera as you traverse through the world.Remix existing worlds into new interpretations, by building on top of their prompts. You can also explore curated worlds in the gallery or in the for inspiration, or build on top of them. And once you’re done, you can download videos of your worlds and your explorations.
Project Genie is an experimental research prototype in Google Labs, powered by Genie 3. As with all our work towards general AI systems, our mission is to build AI responsibly to benefit humanity. Since Genie 3 is an early research model, there are a few known areas for improvement:Generated worlds might not look completely true-to-life or always adhere closely to prompts or images, or real-world physicsCharacters can sometimes be less controllable, or experience higher latency in controlA few of the Genie 3 model capabilities we announced in August, such as promptable events that change the world as you explore it, are not yet included in this prototype. You can find more details on model limitations and future updates on how we’re improving the experience, here.Building on the work we have been doing with trusted testers, we are excited to share this prototype with users of our most advanced AI to better understand how people will use world models in many areas of both AI research and generative media.Access to Project Genie begins rolling out today to Google AI Ultra subscribers in the U.S. (18+), expanding to more territories in due course. We look forward to seeing the infinitely diverse worlds they create, and in time, our goal is to make these experiences and technology accessible to more users.
...
Read the original on blog.google »
Writing about layoffs and the tech market has been on my TODO for several years. Yesterday, the news of 16k Amazon layoffs plus two LinkedIn posts on the same topic back-to-back encouraged me to finally write about it.
Disclaimer: I worked 5 years at Shopify. This is probably why such posts come one after another on my feed but Shopify isn’t the point here, they are just a micro piece of the whole fucked up system.
Tech job market is fundamentally broken and we all pointing fingers at AI.
But having spent almost 2 decades in the industry, I think the rot goes much deeper than ChatGPT.
Truth to be told tech market hasn’t truly ‘improved’ since the 2008 financial crisis. It just mutated into something evil.
After the 2008 mortgage crisis, the economic regime significantly changed. Which was also around the time my interest in Finance began and recently I started to build my own investment tool you can read more about it here.
At that time time we entered an era of extensive liquidity (cheap money). When interest rates are near zero, investors demand growth above all else.
As a result, tech companies stopped building for sustainability and started building for exponential expansion.
Here is a graph shows US Fed Interest Rates by years.
In traditional industries like manufacturing you don’t hire 500 factory workers unless you have a production line that needs them. You don’t over-hire based on a guess.
But in Tech, the playbook is different. Companies over-hire software engineers intentionally. To play the lottery. It is similar to having slow and steady ETF investments vs active investing. No matter how godly you are with active investing sooner or later, you will invest on a loser. Same goes for businesses.
In a factory, “Work in Progress” (unfinished goods) is a liability. You don’t want inventory sitting on the floor; you want it out the door.
In software, we convinced ourselves that “Work in Progress” (hiring engineers to work on projects that haven’t shipped yet) is an asset.
It is not. It is just excessive inventory.
When the market turned, companies realized they were warehousing talent like unsold products. And just like unsold inventory, when the storage costs get too high, you dump it.
Till ~2010, a layoff was a sign of failure. It meant the CEO messed up.
In 2024, a layoff is a signal of “discipline.” Companies lay off thousands, and their stock price jumps.
They are signaling to Wall Street that they are willing to sacrifice human capital to protect margins.
Big Tech companies (think Google, Meta, or any hyper-growth SaaS) operate on a two-tier system:
1. The Core: A fundamental team working on the actual revenue-generating products (the search engine, the ad network, the checkout flow).
2. The Bets: Thousands of engineers hired to build parallel products, experimental features, or simply to keep talent away from competitors and potentially build something that would move into “The Core” tier.
The company knows that most of these side bets will fail. When the economic winds change, the ‘non-core’ staff becomes immediately replaceable.
It’s a vicious cycle: Hire the best people you can find to hoard talent, see what sticks, and lay off the rest when investors want to see better margins.
Most engineers (including me) spent months grinding LeetCode at least twice in their career, studying system design, and passing grueling 6-round interviews to prove they are the “top 1%.”
Yet, once hired, they are often placed on a non-essential team where they become nothing more than a statistic on a spreadsheet.
You jump through hoops to prove you are exceptional, only to be treated as disposable.
For a long time, Europe offered a counter-balance. The pay was lower than Silicon Valley, but the trade-off was stability, stronger labor protections, and a slower, more sustainable pace of work.
As American tech giants expanded into Europe and as European unicorns chased the same growth-at-all-costs playbooks the incentives changed.
Leadership imported US-style compensation models, investor expectations, and organizational volatility, but without importing US-level pay or upside.
”On paper” Europe still has strong labor laws. In practice, companies learned to route around them: constant reorganizations, “strategic refocus” layoffs, performance-managed exits.
The result is the worst of both worlds. European engineers now face US-level job insecurity with European-level compensation and limited mobility. The safety net hasn’t disappeared, but it’s being slowly hollowed out.
And severances… A small, one-time payment is used to justify years of below market compensation, while offering little real protection against sudden displacement.
Europe just became a lower-cost extension of Silicon Valley.
Ultimately, this comes down to how companies signal value.
Traditional businesses used to show their health through revenue, profit, and smart capital investment. Today, Tech companies use layoffs as a marketing signal to Wall Street. They cut costs not because they are going bankrupt, but to show they can be “efficient.”
The more liquidity that was pumped into Tech, the harder this situation became. As long as engineers are treated as speculative assets rather than human capital, the market will remain broken regardless of how good AI gets.
The job market is not “tough” right now because of AI. It is tough because we are unwinding 14 years of financial toxicity.
The liquidity that flooded the tech sector didn’t just inflate valuations; it inflated teams, egos, and expectations.
Until the industry relearns how to build with scarcity rather than excess, the “vicious cycle” of hire-and-dump will continue regardless of how good AI will get.
So you aren’t being laid off because your performance was bad; you are being effectively “liquidated” like a bad stock trade that you sell with a loss.
...
Read the original on substack.com »
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