Justin Mason's Weblog Posts
At a meta level, something I find mildly interesting is how many people [jm: ex-Twitter staff specifically] are writing stuff on Mastodon about how it’s impossible for Mastodon to scale up without using an ad supported model (b/c server costs), it’s better to have ranked feeds because most people want them, etc. The thing I think is interesting is that the people writing this stuff, implicitly, seemingly cannot conceive of a model where the organization is not growth and profit maximizing.
Really valuable info if you’re building resilient services atop AWS; Amazon revealing where their services have cross-region or single-region-of-failure dependencies
ESB Networks is (finally) offering end-users access to their smart electricity meter data with 30-minute granularity
‘Rosetta 2 is remarkably fast when compared to other x86-on-ARM emulators. I’ve spent a little time looking at how it works, out of idle curiosity, and found it to be quite unusual, so I figured I’d put together my notes.’
A little-known detail of the EU Consumer Rights Directive: you have a right to repair or replacement of faulty goods if they fail within 2 years of purchase. The nice thing about this is that so much hardware has built-in obsolescence after only 1 year… you may have to invoke the magic words “EU Consumer Rights Directive” to get this to happen, though. Worth noting that according to one account “the rights only apply in the country of purchase. I’ve had Apple refuse to replace a Magic trackpad that died after 14 months and they would not repair an Airpods case that died after 18 months. I had purchased both in the UK.”
Even better than the EU consumer rights directive!
Under Irish consumer law, consumers are entitled to a free of charge repair or (depending on the circumstances) may be entitled to a replacement, discount or refund by the seller, of defective goods or goods which do not conform with the contract of sale. These rights expire six years from delivery of the goods.
Thought-provoking Mastodon thread about full-scale disaster recovery for large-scale modern software platforms. Here’s a gem:
When I was in Azure, I asked around about what the plan was if “the really big one” hit since deep expertise was nearly totally concentrated in Redmond and, at the time, Azure was guaranteed to have a global outage if a major earthquake incapacitated Redmond. Of course the plan was that there was no real plan and people expected that Azure would have a very extended global outage and an org that was on its way to becoming a $1T business unit would have its value basically wiped out.
Generative AI has had a very good year. Corporations like Microsoft, Adobe, and GitHub are integrating the tech into their products; startups are raising hundreds of millions to compete with them; and the software even has cultural clout, with text-to-image AI models spawning countless memes. But listen in on any industry discussion about generative AI, and you’ll hear, in the background, a question whispered by advocates and critics alike in increasingly concerned tones: is any of this actually legal?
Etsy: “Estimating kWh in the Cloud”:
We thought about how we might be able to estimate our energy consumption in Google Cloud using the data we do have: Google provides us with usage data that shows us how many virtual CPU (Central Processing Unit) seconds we used, how much memory we requested for our servers, how many terabytes of data we have stored for how long, and how much networking traffic we were responsible for. Our supposition was that if we could come up with general estimates for how many watt-hours (Wh) compute, storage and networking draw in a cloud environment, particularly based on public information, then we could apply those coefficients to our usage data to get at least a rough estimate of our cloud computing energy impact. We are calling this set of estimated conversion factors Cloud Jewels. Other cloud computing consumers can look at this and see how it might work with their own energy usage across providers and usage data. The goal is to help cloud users across the industry to help refine our estimates, and ultimately help us encourage cloud providers to empower their customers with more accurate cloud energy consumption data.This is a good interim step, but it’s disappointing how inaccurate the CO2 data exposed by cloud providers is. IMO this needs to be fixed
Interesting — I didn’t realise it was possible to connect to the Mastodon fediverse with such a low-impact service —
A single-user instance with about 100 followers/followees uses somewhere between 50 to 100MB of RAM. CPU usage is only intensive when handling media or processing lots of federation requests.
A new form of COVID-19 misinformation has cropped up in Canada:
The term “immunity debt” is circulating widely online as an explanation for a significant surge in respiratory illness in Canada [… This] hypothesis suggests people’s immune systems are weaker now, due to a lack of exposure to viruses while observing COVID-19 public health measures over the last two-and-a-half years. But this notion […] is simply not true, says Colin Furness, an infection control epidemiologist and assistant professor in the faculty of information at the University of Toronto. “That is, in my estimation, and any immunologist will tell you this, nonsense,” he said. Dr. Samira Jeimy, an allergist and clinical immunologist at St Joseph’s Health Care London, agrees, saying the idea that one’s immune system can be weakened due to lack of exposure to illness “shows a basic lack of understanding of how the immune system works.” “There’s almost like an old wives tale, that you need to get sick to develop a healthy immune system. That’s actually not true.”
“Will AI image generators kill the stock image industry? It’s a question asked by many following the rise of text-to-image AI models in recent years. The answer from the industry’s incumbents, though, is “no” — not if we can start selling AI-generated content first. Given that Shutterstock licensed data to OpenAI to train DALL-E in 2021, it means that the model’s output will soon be competing with the same individuals whose content it relies on. At the same time, Shutterstock is also officially banning users from selling AI generated content on its platform. The company’s rationale is that it can’t validate the copyright of this output. However, it also means contributors won’t be able to compete with its own AI art services.” Great, I am looking forward to some really shitty AI output cropping up in stock images in the near future….
Cheery stuff from the Bulletin of Atomic Scientists, based on updated modelling
They would, naturally….
“There are online services that, purportedly using artificial intelligence (AI), extract, or rather, copy, the vocals, instrumentals, or some portion of the instrumentals from a sound recording, and/or generate, master or remix a recording to be very similar to or almost as good as reference tracks by selected, well known sound recording artists […] To the extent these services, or their partners, are training their AI models using our members’ music, that use is unauthorized and infringes our members’ rights by making unauthorized copies of our members works. In any event, the files these services disseminate are either unauthorized copies or unauthorized derivative works of our members’ music”
“Okay, the time has come, it’s been an entire decade, let’s talk about loadbalancing techniques and how they evolved at Google in response to various practical failure modes, from 2008 to 2012.” This thread is great. A solid history of Google’s use of various load balancing techniques, ranging from N+1 service duplication with implicit failover rules, modern-service-mesh-style proxying, client-side builtin load balancing libs, followed by local sidecars which downloaded routing assignment configs periodically and operated mainly offline.
tl;dr: 6.2% average rate, more women than men, 15% continued to suffer after 12 months.
A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months.
Fascinating interview with Dr. Marsha Wills-Karp, an expert on the environmental determinants of immune diseases:
Almost no virus is protective against allergic disease or other immune diseases. In fact, infections with viruses mostly either contribute to the development of those diseases or worsen them. The opposite is true of bacteria.Pets are good, though:
We’ve also noticed that people who live on farms have fewer of these diseases because they’re exposed to — for lack of a better term — the fecal material of animals. And what we have found is that it’s due to these commensal bacteria. That is one of the components that helps us keep a healthy immune system. Most of us will probably not adopt farm life. But we can have a pet, we can have a dog.
Good online source for buying batteries/power banks online for phone charging, etc., now that Amazon won’t ship batteries to Ireland for some reason :| Banggood.com reportedly have an EU warehouse these ship from. (via ITC Slack)
The staggeringly obvious, confirmed: ‘We analyzed transmission patterns associated with 976 SARS-CoV-2 exposure events, involving 460 positive individuals, as identified in early 2021 through routine surveillance and an extensive screening conducted on students, school personnel, and their household members in a small Italian municipality. […] From the analysis of 152 clear infection episodes and 584 exposure events identified by epidemiological investigations, we estimated that approximately 50%, 21%, and 29% of SARS-CoV-2 transmission was associated with household, school, and community contacts, respectively. […] A higher proportion of infected individuals causing onward transmission was found among students (46.2% vs. 25%, on average), who also caused a markedly higher number of secondary cases’ Ah, remember 2020, 2021, and indeed 2022, when the Irish department of education and HSE were vehement that COVID-19 didn’t spread in schools….
So many good lines in this article from Ed Yong — “calling the pandemic “over” is like calling a fight “finished” because your opponent is punching you in the ribs instead of the face”.
“The allure of biomedical panaceas is still strong. For more than a year, the Biden administration and its advisers have reassured Americans that, with vaccines and antivirals, “we have the tools” to control the pandemic. These tools are indeed effective, but their efficacy is limited if people can’t access them or don’t want to, and if the government doesn’t create policies that shift that dynamic.” “Technological solutions also tend to rise into society’s penthouses, while epidemics seep into its cracks. Cures, vaccines, and diagnostics first go to people with power, wealth, and education, who then move on, leaving the communities most affected by diseases to continue shouldering their burden.” “America has little chance of effectively countering the inevitable pandemics of the future; it cannot even focus on the one that’s ongoing.”
Some top notch Reddit suggestions here
ffs. More tedious systemd bullshit — it slows boot to allow a “daily” cron job to run. And this has been the case since 2017?
sudo systemctl disable apt-daily.service sudo systemctl disable apt-daily.timer sudo systemctl disable apt-daily-upgrade.timer sudo systemctl disable apt-daily-upgrade.service
safe rehab approaches for long COVID sufferers. I’m just bookmarking here; hoping nobody I know has to put this into practice
Extremely detailed writeup of how k8s handle’s out of memory conditions, how the Linux OOM killer interacts with cgroups, and application runtime implications
Is Google experimenting with a new (and broken) compression algorithm?
Over the weekend, people began noticing that their years-old photos (over five years, approximately) have lines and deep cracks running through them, as well as other blurry or distorted areas. White dots are also a common occurrence. Some images are more damaged than others with seemingly no pattern to what’s impacted or the severity. It’s weirdly somewhat analogous to physical water damage, with reports across Google Photos for Android, iOS, and the web. According to those affected, the corruption persists when downloading the image. This apparently applies to both individual downloads and when using Google Takeout. The original copies of pictures do not appear to be impacted, but the edited ones are what appear in the Google Photos apps.
Commentary on the study led by Ziyad Al-Aly at the Washington University School of Medicine from an Aussie immunologist —
the researchers found that the risk of heart, brain, kidney and blood complications all increased with each subsequent infection. As Goodnow has noted about the findings, “The risk of cardiovascular disease, for example, increased after one infection, but doubled in people who had two infections, and tripled in those who had been infected thrice.” Similar risks were found for heart disease, blood clotting problems, brain decline and diabetes. Nor did vaccines seem to help in preventing these problems, which most frequently occur up to six months after infection. “Every time you dip your bucket in that COVID well, you’ve got the same chance of a whole lot of bad things happening,” explained Goodnow, who considers the veterans study “really important real-world data.” His takeaway: “COVID-19 is not just a cold, and having it before doesn’t ‘get it over with.’”
The British Medical Journal’s set of guidelines for GPs and primary care providers on how to help long covid sufferers — gives a good idea of the current state of affairs for this tricky syndrome.
What you need to know: Long covid (prolonged symptoms following covid-19 infection) is common; The mainstay of management is supportive, holistic care, symptom control, and detection of treatable complications; Many patients can be supported effectively in primary care by a GP with a special interest.
Our results show that in the postacute phase of COVID-19, there was increased risk of an array of incident neurologic sequelae including ischemic and hemorrhagic stroke, cognition and memory disorders, peripheral nervous system disorders, episodic disorders (for example, migraine and seizures), extrapyramidal and movement disorders, mental health disorders, musculoskeletal disorders, sensory disorders, Guillain–Barré syndrome, and encephalitis or encephalopathy. We estimated that the hazard ratio of any neurologic sequela was 1.42 (95% confidence intervals 1.38, 1.47) and burden 70.69 (95% confidence intervals 63.54, 78.01) per 1,000 persons at 12 months. The risks and burdens were elevated even in people who did not require hospitalization during acute COVID-19.
Nice terminology — similar to the “thundering herd” problem seen in distributed systems. “A type of cascading failure that can occur when massively parallel computing systems with caching mechanisms come under very high load. This behaviour is sometimes also called dog-piling”
Where we are with COVID boosters:
There’s ample evidence that a 3rd shot or 4th shot (1st or 2nd booster) will help provide important protection, and that is especially vital for people age 50+, with ample support for the recommendation for all age 12 and older to get boosters. The right question is about the 5th booster, for which there are no clinical data yet, but will likely extend a high level of protection against severe Covid. But 4 or 6 months isn’t going to cut it as a public health protection policy, as there will be further attrition of interest and uptake for boosters as we go forward. Fortunately, we’re declining in cases and will likely experience a fairly quiescent phase (further descent, no surge) with respect to infections and hospitalizations for the next couple of months until BA.2.75.2 gets legs (or an alternative BA.2 derivative). Now is the time to stop chasing SARS-CoV-2 and start mounting an aggressive get- ahead strategy. There’s the intertwined triad to contend with: more immune escape, more evidence of imprinting, and the inevitability of new variants that are already laying a foundation for spread. Enough of the booster after booster, shot-centric approach; it has been formidable, lifesaving, sickness-avoiding, and essential as a bootstrap, temporizing measure. Now we need to press on with innovation for more durable, palatable, and effective solutions. They are in our reach.
‘Fix Twitter video embeds in Discord (and Telegram)’ — work around bugs in those platforms which break Twitter embedding. Just replace ‘twitter.com’ with ‘vxtwitter.com’ and problem solved
‘a modern OSS Key-Value store built for today’s hardware’, looks nicely optimized
Postgres distributed scaler software now fully OSS
LinkedIn are using Pinot for this use case, with a super-low-latency querying requirement:
InFlow requires storage of tens of TBs of data with a retention of 30 days. To support its real-time troubleshooting use case, the data must be queryable in real-time with sub-second latency so that engineers can query the data without any hassles during outages. For the storage layer, InFlow leverages Apache Pinot.
‘As AI-generated art platforms like DALL-E 2, Midjourney, and Stable Diffusion explode in popularity, online communities devoted to sharing human-generated art are forced to make a decision: should AI art be allowed?’ This makes sense, IMO — or at least sideline them to their own parts of the forum… (via Waxy)
“I discovered this woman, who I call Loab, in April. The AI reproduced her more easily than most celebrities. Her presence is persistent, and she haunts every image she touches. CW: Take a seat. This is a true horror story, and veers sharply macabre.” Top-notch creepypasta, and/or real-world creepiness; either a ghost in the codec as Hari Kunzru would put it, or — let’s face it — AI’s haunted
LOL, this is madness. Move fast and forget everything:
In the March 2022 hearing, Zarashaw and Steven Elia, a software engineering manager, described Facebook as a data-processing apparatus so complex that it defies understanding from within. The hearing amounted to two high-ranking engineers at one of the most powerful and resource-flush engineering outfits in history describing their product as an unknowable machine. […] The fundamental problem, according to the engineers in the hearing, is that […] the company never bothered to cultivate institutional knowledge of how each of these component systems works, what they do, or who’s using them.
Various AWS queueing/messaging services’ latencies compared in eu-west-1: ‘When latency matters, there are a few obvious winners. SQS Standard can deliver a message to a consumer in as fast as 14 ms and is seldomly slower than 100 ms, assuming low batch sizes. Kinesis with Enhanced Fan-Out is only slightly slower and allows for multiple consumers and a long history of events. SNS falls in the low latency category too, although the SNS FIFO option includes more moving parts and thus a larger latency spread, up to half a second. Step Functions and DynamoDB Streams take up the middle section, with P50 latencies up to about 200 ms. The highest latency is introduced by EventBridge and Kinesis Data Streams without Enhanced Fan-Out. These services add at least a few hundred milliseconds to your integrations, but can easily run up to a second or more.’
home energy monitoring using HomeAssistant, MQTT, and a set of power-monitoring smart plugs preflashed with the open Tasmota firmware. This is all very practical, and the power-socket-based approach means no rewiring is necessary. I think this is the best UI I’ve seen so far for a home energy optimization system
tl;dr: “Lithium-ion and lithium-polymer batteries should be kept at charge levels between 30% and 70% at all times. Full charge/discharge cycles should be avoided if possible. Exceptions to this can be made occasionally to readjust the charge controller and battery capacity meter. Modern batteries do not have to be conditioned, and are at peak capacity out of the box. If you need to store batteries for long time periods you should charge them to roughly half their capacity and put them in the fridge. Very high and very low temperatures should be avoided, particularly while charging. When choosing a charger quality is key, and high quality chargers are by and large interchangeable.”
EFF post on a data broker being misused by US police for warrantless “dragnet” surveillance:
Fog claims that their product is made of [location] data willingly given by people. But people did not hand their geolocation data over to Fog or the police, willingly or even knowingly. Rather, they gave it over, for example, to a weather app so that they could see if it will rain in their town today. When they downloaded the app, they may have clicked a box purporting to grant various so-called “consents,” but no reasonable person expects this will result in the app tracking all their movements, the app developer selling this sensitive information to a data broker, and police ultimately buying it.and this is why the GDPR is so valuable.
This is some fantastic symmetry! Years ago, I took the BLAST bioinformatics algorithm, normally used to spot correlations between DNA/RNA sequences, and applied it to correlate and detect spam. And now here’s UMAP, an algorithm used to correlate and detect malware and viruses, going in the opposite direction!
When mathematicians Leland McInnes and John Healy walked into their work’s annual “Big Dig” — a sort of classified hackathon for Canada’s version of the National Security Agency — in 2017, they were not thinking about biology at all. They wanted to find a way to quickly spot the differences between computer viruses. They ended up creating a tool to simplify datasets and visualize the data points in them: an algorithm they named Uniform Manifold Approximation and Projection, or UMAP. They published a paper on it in 2018. To their great surprise, in fewer than five years, it has become one of the most ubiquitous tools in modern biology research. UMAP has now been used to study everything from forecasting rain in the Alps to identifying the many-hued pigments in a Gauguin artwork to modeling how Covid-19 tweets are disseminated. And, of course, scientists have applied UMAP to studying the actual virus itself. The technique is now the method of choice for most computational biologists who want to see what, exactly, is going on in a dataset.
Solid tip from Nitsan Wakart on Twitter:
If you are profiling for a CPU bottleneck [in java], DO NOT RELY ON JVM FLIGHT RECORDER METHOD PROFILING. Not even a little bit. Use `async-profiler` for profiling(`-e cpu,lock,alloc`), with `–jfrsync default/profile` for extra JVM/JDK events.
I had no idea that the beer slushy had been so perfected in Japan:
In March 2012, Kirin pushed beer even closer to Arctic climes with the Ichiban Shibori Frozen Draft: a draft beer topped with a multi-inch-thick angelic swirl of marshmallow fluff-like frozen foam. At that time, ice-cold beer was booming in Japan, says Tsuneo Mitsudomi, president of Kirin Brewery of America, and Kirin sought its own take on the trend that also served a practical purpose. Inspired by a frozen smoothie machine found in Italy, Kirin lab techs developed technology capable of whipping up beer—yes, 100 percent beer—into a froyo-like state. “We focused on the function which can keep beer cold, and magical looks,” Mitsudomi says. Kirin spent roughly five years perfecting the texture and temperature (23 degrees Fahrenheit) of its frozen foam. An unorthodox lid, the layer of frozen foam not only prevents carbonation from quickly escaping, but also insulates the glass for 30 minutes, more time than it takes to polish off the average pint.Also “jelly beer”, excellent tech from Thailand:
At Uncle Boons, the beer slushy takes the form of bia wun, or jelly beer. Unlike other beer slushies, jelly beer is shaped and served in the bottle. A motorized barrel sourced from Thailand is filled with ice, water and salt, the bottles placed within. The whole apparatus gently rocks back and forth with the enthusiasm of a small dingy to agitate its contents. The salted water drops to 27 degrees, while the pressure inside the bottle keeps the beer from freezing over and exploding. Once the beer is removed from the barrel, the bottle is given a shake, then a sharp tap, and ice crystals begin to form within. As the cap is removed, the beer starts to froth and foam, freezing over in a molecular equation most often described as magic.
On a larger scale, Google Cloud is already at 100% carbon neutrality, apparently via offsets and a few other accounting approaches, with a goal to move to 100% renewable energy for all cloud regions by 2030. Meanwhile, AWS’s carbon footprint tool is an embarrassment to AWS and its stated goal of reaching 100% renewable energy usage by 2025. The bottom line: One of these carbon neutrality approaches is indicative of a thoughtful approach to partnering with customers to lead to a better climate story around cloud usage. The other appears to have been phoned in by clowns the night before it was due.
The world is awash in bullshit. Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit — and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit. We’re sick of it. It’s time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument.
“Open Source Continuous Profiling Platform” — continuous profiling, as has been used in companies like Twitter for a while. This looks pretty practical though, due to some key features:
Lightning Fast – Doesn’t matter if you’re looking at 10 seconds or 10 months of profiling data — the queries are always fast thanks to our custom designed storage engine. Minimum Overhead – Pyroscope doesn’t affect performance of your applications, thanks to the use of sampling profiling technology. Cost-Effective – Pyroscope uses a custom data storage engine and stores profiling data very efficiently, making it economically viable to store profiling data from all of your apps for years.
this is nifty, a combo COVID/flu rapid test… might be handy to have a few set aside for the winter
As Nelson puts it: “guy submits an AI-generated art piece to the Colorado State Fair, wins prize” —
“Technology is increasingly deployed to make gig jobs and to make billionaires richer, and so much of it doesn’t seem to benefit the public good enough,” cartoonist Matt Borrs told Warzel in a follow up piece. “AI art is part of that. To developers and technically minded people, it’s this cool thing, but to illustrators it’s very upsetting because it feels like you’ve eliminated the need to hire the illustrator.”
Interesting dive into the training set:
Stable Diffusion’s initial training was on low-resolution 256×256 images from LAION-2B-EN, a set of 2.3 billion English-captioned images from LAION-5B‘s full collection of 5.85 million image-text pairs, as well as LAION-High-Resolution, another subset of LAION-5B with 170 million images greater than 1024×1024 resolution (downsampled to 512×512). Its last three checkpoints were on LAION-Aesthetics v2 5+, a 600 million image subset of LAION-2B-EN with a predicted aesthetics score of 5 or higher, with low-resolution and likely watermarked images filtered out. For our data explorer, we originally wanted to show the full dataset, but it’s a challenge to host a 600 million record database in an affordable, performant way. So we decided to use the smaller LAION-Aesthetics v2 6+, which includes 12 million image-text pairs with a predicted aesthetic score of 6 or higher, instead of the 600 million rated 5 or higher used in Stable Diffusion’s training.
‘”This spreadsheet provides a way for AWS cloud users to estimate the carbon footprint of their EC2 based workloads. Two estimations are available: – Carbon emissions related to running the instance, including the datacenter PUE – Carbon emissions related to manufacturing the underlying hardware.’ Courtesy of French online ad company Teads
“There were 15 years on the internet that were unlike anything else, and that I don’t think you’ll be able to really get unless you were there,” Le Conte tells me. The world she outlines was one inhabited by loners and misfits, where awkward teenagers could go to find themselves, reach out to people across the world with shared interests, create their own communities, forge new identities – anonymously and without adult supervision. It was dangerous, yes – she admits that young people probably should never have been given that much freedom – but also liberating and, above all, fun. So much fun, in fact, that soon it wasn’t just the weird kids who wanted to be part of it. She compares what happened next to a group of children building a treehouse to play in. “And then all their parents joined in and were like, ‘Hello, we hear you have a treehouse. We live here as well now.’”
Twitter thread with a good explanation of this scary attack; essentially Chinese “advertisers” were uploading custom audiences containing a bunch of random ids and one actually-targeted user’s id, then pinpointing the user activity that way
‘my attempt to put use cases for clean hydrogen – whether it be green, blue, pink, turquoise or whatever – into some sort of merit order, because not all are equally likely to succeed. […] Clean hydrogen will have to win its way into the economy, use case by use case. It could do so on its merits, or it could do so because of supportive policy (including carbon prices). But it will have to do so in competition with every other clean technology that could solve the same problem. And that is where the dreams of the hydrogen economy hit reality: in almost all use cases there is a good reason why hydrogen is not currently used – because other solutions are cheaper, simpler, safer or more convenient.’ (via Chris Adams)