Artsy’s Technology Choices evaluation process
This is a nice way to evaluate new technology options, from Artsy:
We want to accomplish a lot with a lean team, which means we must choose stable technologies. However, we also want to adopt best-of-breed technologies or best-suited tools, which may need work or still be evolving. We’ve borrowed from ThoughtWorks’ Radar to define the following stages for evaluating, adopting, and retiring technologies:
- Adopt: Reasonable defaults for most work. These choices have been exercised successfully in production at Artsy and there is a critical mass of engineers comfortable working with them.
- Trial: These technologies are being evaluated in limited production circumstances. We don’t have enough production experience to recommend them for high-risk or business-critical use cases, but they may be worth consideration if your project seems like a fit.
- Assess: Technologies we are interested in and maybe even built proofs-of-concept for, but haven’t yet trialed in production.
- Hold: Based on our experience, these technologies should be avoided. We’ve found them to be flawed, immature, or simply supplanted by better alternatives. In some cases these remain in legacy production uses, but we should take every opportunity to retire or migrate away.
(Via Lar Van Der Jagt on the Last Week In AWS slack instance)
Tags: via:lwia tech technology radar choices evaluation process architecture planning tools
Justin's Linklog Posts
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Some good thoughts from a SlateDB dev, regarding initial principles for errors in SlateDB, derived from experience with Kafka:
- Keep public errors separate from internal errors. The set of public errors should be kept minimal and new errors should be highly scrutinized. For internal errors, we can go to town since they can be refactored and consolidated over time without affecting the user.
- Public errors should be prescriptive. Can an operation be retried? Is the database left in an inconsistent state? Can a transaction be aborted? What should the user actually do when the error is encountered? The error should have clear guidance.
- Prefer coarse error types with rich error messages. There are probably hundreds of cases where the database can enter an invalid state. We don’t need a separate type for each of them. We can use a single FatalError and pack as much information into the error message as is necessary to diagnose the root cause.
(via Chris Riccomini)
Tags: errors api design slatedb api-design error-handling exceptions architecture
7 Lessons from building a small-scale AI application
These are good. tl;dr:
- AI programming is stochastic;
- Data quality is real work;
- Models are only as good as the evaluation;
- Trust/Quality is the #1 issue;
- Your training pipeline is your core IP;
- AI is yet another distributed system;
- Don’t buy the AI library hype
via Niall Murphy.
Optimizing Java Apps on Kubernetes
“Optimizing Java Applications on Kubernetes: beyond the Basics”: Bruno Borges, at the InfoQ Dev Summit Boston, discusses the strategies for enhancing Java application performance on Kubernetes, focusing on leveraging JVM ergonomics, and managing garbage collection processes. Some interesting tips here.
Tags: kubernetes java eks resources ops scaling scalability gc optimization jvm
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Bookmarking this in case I have to use it; I have a blog-related use case that I don’t want LLM scrapers to kill my blog with.
Anubis is a man-in-the-middle HTTP proxy that requires clients to either solve or have solved a proof-of-work challenge before they can access the site. This is a very simple way to block the most common AI scrapers because they are not able to execute JavaScript to solve the challenge. The scrapers that can execute JavaScript usually don’t support the modern JavaScript features that Anubis requires. In case a scraper is dedicated enough to solve the challenge, Anubis lets them through because at that point they are functionally a browser.
The most hilarious part about how Anubis is implemented is that it triggers challenges for every request with a User-Agent containing “Mozilla”. Nearly all AI scrapers (and browsers) use a User-Agent string that includes “Mozilla” in it. This means that Anubis is able to block nearly all AI scrapers without any configuration.
Tags: throttling robots scraping ops llms bots hashcash tarpits
Cost-optimized archival in S3 using s3tar
“s3tar” is new to me, and looks like a perfect tool for this common use-case — aggregation and archival of existing data on S3, which often requires aggregation into large file sizes to take advantage of S3 Glacier storage classes (which have a minimum file size of 128Kb).
s3tar optimizes for cost and performance on the steps involved in downloading the objects, aggregating them into a tar, and putting the final tar in a specified Amazon S3 storage class using a configurable “–concat-in-memory” flag. … The tool also offers the flexibility to upload directly to a user’s preferred storage class or store the tar object in S3 Standard storage and seamlessly transition it to specific archival classes using S3 Lifecycle policies.
The only downside of s3tar is that it doesn’t support recompression, which is also a common enough requirement — especially after aggregation of multiple small input files into a larger, more compressible archive. But hey, can’t have everything.
s3tar: https://github.com/awslabs/amazon-s3-tar-tool
Tags: s3tar amazon s3 compression storage archival architecture aggregation logs glacier via:lwia
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It’s great to see pushback against React, Angular, and other SPA architectures for web app delivery. I never got my head around the applicability of these for many web app use cases so this is just confirming my biases :)
Related Mastodon thread: https://toot.cafe/@slightlyoff/113868445222841008
Tags: react angular spa web-apps webdev javascript html apps
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Since 2019 (!), the AWS load balancer controller component doesn’t safely handle pod shutdowns when the ALB target-type is set to
ip. This is the bug report, still open…Tags: aws load-balancing alb eks kubernetes ops bugs
Cryptocurrency “market caps” and notional value
Excellent explainer from Molly White, which explains the risk around quoting “market caps” for memecoins:
The “market cap” measurement has become ubiquitous within and outside of crypto, and it is almost always taken at face value. Thoughtful readers might see such headlines and ask questions like “how did a ‘$2 trillion market’ tumble without impacting traditional finance?”, but I suspect most accept the number.
When crypto projects are hacked, there are headlines about hackers stealing “$166 million worth” of tokens, when in reality the hackers only could cash out 2% of that amount (around $3 million) because their attempts to sell illiquid tokens caused the price to crash.
Tags: molly-white memecoins bitcoin rug-pulls scams liquidity market-caps cryptocurrency
Implementing A Byte Pair Encoding (BPE) Tokenizer From Scratch
A discussion of the popular byte pair encoding (BPE) tokenization algorithm, which is used in large language models like GPT-2 to GPT-4, Llama 3, etc. to tokenize text. The BPE algorithm was originally described in 1994: “A New Algorithm for Data Compression” by Philip Gage.
Tags: encoding text bpe llms algorithms tokenization parsing
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“A federated microblogging software for single users. ActivityPub-enabled, Mastodon-compatible API, supports CommonMark and Misskey-style quotes. Hollo is designed for single-users, so you can own your instance and have full control over your data. It’s perfect for personal microblogs, notes, and journals.”
Seems fairly heavyweight, however, so I probably won’t be running it, but it’s a nice take on the single-user-server Fediverse use case.
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The Irish National Transport Authority have an open data API for realtime public transport information; very cool. “The GTFS-R API contains real-time updates for services provided by Dublin Bus, Bus Éireann, and Go-Ahead Ireland.”
The specification currently supports the following types of information:
Trip updates – delays, cancellations, changed routes; Service alerts – stop moved, unforeseen events affecting a station, route or the entire network; Vehicle positions – information about the vehicles including location and congestion level
Registration is required.
Tags: public-transport buses trains transit nta gtfs apis open-data dublin ireland
Five things privacy experts know about AI
Damien Desfontaines writes some really interesting stuff about Differential Privacy in AI training, and how bad the current situation is with large language models
Tags: llms ai differential-privacy damien-desfontaines privacy training anonymisation memorization
Why the British government is so into AI
Interesting BlueSky thread on the topic —
The UK Government believes several things:
1) The AI genie is out of the bottle and cannot be put back in
2) Embracing AI would definitely be good for the British economy
3) Enforcing copyright on AI training would put Britain out of step with rest of the world and subsequently…
4) Enforcing copyright would be ineffective as AI would just be trained elsewhere, cutting out Brit creatives entirely
5) Govt’s preferred option is permissive enough to be attractive to AI firms but demands transparency so at least rights holders have some recourse; the alternative is bleaker.
Obviously, I contest all of these beliefs to one degree or another, but this is where the govt is, and it’s useful to understand that. The real crux of the debate, as they see it, is how Britain’s laws can practically deal with the global inevitability of AI. They believe it’s untenable to make Britain a legislative pariah state for AI, and that this would not lead to good outcomes for British creatives anyway. This is a point worth considering when replying to the consultation.
However, the govt says it’s not going to implement policy before it has a technical solution for rights holders to opt-out and chase down infringements. My view is that this is difficult to the point of being pure fantasy, and either means that the govt is not serious about finding a real, effective technical solution, or this policy will be kicked indefinitely down the road. My dinner partner was optimistic a solution could be achieved within the timespan of a year or two. I just don’t buy it.
Government says it has not sided with AI firms over creative industries. However, its understanding of “not taking a side” creates a false equality between massive companies whose business relies on crime and individuals whose livelihoods will be destroyed.
I got the sense that there is no political will whatsoever to seriously challenge firms who offer to spend big in Britain, and that any thought of holding them to account for actual crime is simply considered naive. But we do have a bit of time while govt attempts to confect their magical, easy to use, opt-out solution—time during which one or several of these AI firms might implode, making the true cost more apparent.
Tags: uk government ai policy copyright ip britain economy future
The people should own the town square
Ah, this is welcome news from Mastodon:
We are going to transfer ownership of key Mastodon ecosystem and platform components to a new non-profit organization, affirming the intent that Mastodon should not be owned or controlled by a single individual. […] Taking the first tentative steps almost a year ago, there are already multiple organizations involved with shepherding the Mastodon code and platform. The next 6 months will see the transformation of the Mastodon structures, shifting away from the early days’ single-person ownership and enshrining the envisioned independence in a dedicated European not-for-profit entity.
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As a modern option for observability through service metrics, ClickHouse seems to be decent as a self-hosted option, integrating with Grafana as described here and collecting data from OpenTelemetry instrumentation in service code. (By many accounts, this avoids some not great design decisions made in Prometheus.) Bookmarking for reference…
Tags: telemetry metrics service-metrics clickhouse sql grafana observability opentelemetry
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Nice to see an important public need being met here:
The [Watch Duty] app gives users the latest alerts about fires in their area [in California] and has become a vital service for millions of users in the western U.S. struggling with the seemingly constant threat of deadly wildfires—one major reason it had over 360,000 unique visits from 8:00-8:30 a.m. local time Wednesday. And the man behind Watch Duty promises that as a nonprofit, his organization has no plans to pull an OpenAI and become a profit-seeking enterprise.
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this is a great Steve Jobs story, from the engineer who wrote v1 of the MacOS X Dock:
At one point during a trip over, Steve was talking to Bas and asked how things were coming along with the Dock. He replied something along the lines of “going well, the engineer is over from Ireland right now, etc”. Steve left, and then visited my manager’s manager’s manager and said the fateful words (as reported to me by people who were in the room where it happened).
“It has come to my attention that the engineer working on the Dock is in FUCKING IRELAND”.
I was told that I had to move to Cupertino. Immediately. Or else.
I did not wish to move to the States. I liked being in Europe. Ultimately, after much consideration, many late night conversations with my wife, and even buying a guide to moving, I said no.
They said ok then. We’ll just tell Steve you did move.
(via Niall Murphy)
Light Bars for Zoom / Video Conference
recommended by someone on ITC Slack; improves videoconference lighting nicely
Tags: video slack videoconferences lighting work
Court docs allege Meta trained LLM models using pirated book trove
This is pretty massive:
The [court] document claims that Meta decided to download documents from Library Genesis — aka. “LibGen” — to train its models. LibGen is the subject of a lawsuit brought by textbook publishers who believe it happily hosts and distributes [pirated] works [….]
The filing from plaintiffs in the Kadrey case claims that documents produced by Meta […] describe internal debate about accessing LibGen, a little squeamishness about using BitTorrent in the office to do so, and eventual escalation to “MZ” [Mark Zuckerberg himself], who approved use of the contentious resource. […]
Another filing claims that a Meta document describes how it removed copyright notifications from material downloaded from LibGen, and suggests the company did so because it realized including such text could mean a model’s output would reveal it was trained on copyrighted material.
US District Court Judge Vince Chhabria also noted that in one of the documents Meta wants to seal, an employee wrote the following:
“If there is media coverage suggesting we have used a dataset we know to be pirated, such as LibGen, this may undermine our negotiating position with regulators on these issues.”
No shit.
Tags: piracy meta copyright mark-zuckerberg law llama training libgen books
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A handy tool to test your internet connection for “bufferbloat”, the error condition involving “undesirable high latency caused by other traffic on your network. It happens when a flow uses more than its fair share of the bottleneck. Bufferbloat is the primary cause of bad performance for real-time Internet applications like VoIP calls, video games, and videoconferencing.”
(My home internet connection is currently rating a C: “your latency increased considerably under load”, jumping from a min/mean/p95/max of 10.7, 16.9, 23.7, 30.1ms to 35.3, 98.4, 121.0, 286.0ms under load, yikes, so looks like I need to do some optimising.)
Tags: bufferbloat internet networking optimisation performance testing tools
Waymos don’t stop for pedestrians
Ah here.
“Waymo (aka Google) admits that it trains its robotaxis to break the law. When a Washington Post reporter finds robotaxis fail to stop for pedestrians in marked crosswalk 70% of the time, Waymo says it follows “social norms” rather than laws.
Expert explains: When robotaxis obey law, they don’t go fast enough to compete successfully with Uber, so Google execs ordered engineers to ignore laws.”
Tags: google waymo laws pedestrians safety crosswalks crossings road-safety self-driving-cars
Garbage Day on Meta’s moderation plans
This is 100% spot on, I suspect, regarding Meta’s recently-announced plans to give up on content moderation:
After 2021, the major tech platforms we’ve relied on since the 2010s could no longer pretend that they would ever be able to properly manage the amount of users, the amount of content, the amount of influence they “need” to exist at the size they “need” to exist at to make the amount of money they “need” to exist.
And after sleepwalking through the Biden administration and doing the bare minimum to avoid any fingers pointed their direction about election interference last year, the companies are now fully giving up. Knowing the incoming Trump administration will not only not care, but will even reward them for it.
The question now is, what will the EU do about it? This is a flagrant raised finger in the face of the Digital Services Act.
Tags: moderation content ugc meta future dsa eu garbage-day
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Via Susie Dent, Word of the Day is ‘uhtcearu’ [ucht-kay-aru, with the ‘ch’ as in the Scottish ‘loch’]: Old English for ‘the sorrow before dawn’, when you lie awake in the darkness and worries crowd your mind.
It’s amazing to realise that this unpleasant phenomenon of neurochemistry is a thing that’s been around for thousands of years.
See also https://www.reddit.com/r/OldEnglish/comments/e7su8n/what_is_the_proper_form_of_uhtceare/
Tags: brains worry words uhtcearu uhtceare dawn morning neurochemistry
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“Some of my favorite tricks for finding RSS Feeds to follow”, from George Hotelling. These are great, particularly RSS Bridge (via Nelson)
Tags: via:nelson syndication feeds rss blogs reddit youtube
ads.txt for a site with no ads
Don Marti: “since there’s a lot of malarkey in the online advertising business, I’m putting up this file [on my website] to let the advertisers know that if someone sold you an ad and claimed it ran on here, you got burned.”
The format is defined in a specification from the IAB Tech Lab. The important part is the last line. The placeholder is how you tell the tools that are supposed to be checking this stuff that you don’t have ads.
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“Quickly save links, notes, and images and hoarder will automatically tag them for you using AI for faster retrieval. Built for the data hoarders out there!”
Self-hosted (with a docker-compose file), open-source link hoarding tool; intriguingly, this scrapes links, extracts text and images, generates automated tag suggestions using OpenAI or a local ollama LLM, and indexes the page’s full text using Meilisearch, which seems to be a speedy incremental search. Could be a great place to gateway links from this blog into a super-searchable form. hmm
Tags: links archiving bookmarks web search hoarder docker ai
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A very thought-provoking essay from Evgeny Morozov on AI, LLMs and their embodied political viewpoint:
Sure, I can build a personalized language learning app using a mix of private services, and it might be highly effective. But is this model scalable? Is it socially desired? Is this the equivalent of me driving a car where a train might do just as well? Could we, for instance, trade a bit of efficiency and personalization to reuse some of the sentences or short stories I’ve already generated in my app, reducing the energy cost of re-running these services for each user?
This takes us to the core problem with today’s generative AI. It doesn’t just mirror the market’s operating principles; it embodies its ethos. This isn’t surprising, given that these services are dominated by tech giants that treat users as consumers above all. Why would OpenAI, or any other AI service, encourage me to send fewer queries to their servers or reuse the responses others have already received when building my app? Doing so would undermine their business model, even if it might be better from a social or political (never mind ecological) perspective. Instead, OpenAI’s API charges me— and emits a nontrivial amount of carbon emissions— even to tell me that London is the capital of the UK or that there are one thousand grams in a kilogram.
For all the ways tools like ChatGPT contribute to ecological reason, then, they also undermine it at a deeper level—primarily by framing our activities around the identity of isolated, possibly alienated, postmodern consumers. When we use these tools to solve problems, we’re not like Storm’s carefree flâneur, open to anything; we’re more like entrepreneurs seeking arbitrage opportunities within a predefined, profit-oriented grid. [….]
The Latin American examples give the lie to the “there’s no alternative” ideology of technological development in the Global North. In the early 1970s, this ideology was grounded in modernization theory; today, it’s rooted in neoliberalism. The result, however, is the same: a prohibition on imagining alternative institutional homes for these technologies. There’s immense value in demonstrating—through real-world prototypes and institutional reforms—that untethering these tools from their market-driven development model is not only possible but beneficial for democracy, humanity, and the planet.
Tags: technology ai history eolithism neoliberalism llms openai cybernetics hans-otto-storm cybersyn
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From AWS VP of Technology, Mae-Lan Tomsen Bukovec — a set of roles which a Principal Engineer can play to get projects done:
Sponsor: A Sponsor is a project/program lead, spanning multiple teams. Yes, this role can be played by a manager but it does not have to be (at least not at Amazon). If you are a Sponsor, you have to make sure decisions are made and that people aren’t stuck in analysis paralysis. This doesn’t mean that you yourself make those decisions (that’s often a Tie-breaker’s role which you may or may not be here). But you have to drive making sure decisions get made, which can mean owning those decisions, escalating to the right people, or whatever it takes to get it done.
A Sponsor is constantly clearing obstacles and getting things moving. It is a time-consuming role. You shouldn’t have time to act as Guide or a Sponsor on more than two projects combined, and you don’t have to be a Sponsor every year. But if a few years go by, and you haven’t been a Sponsor, it might be time to think about where you can step in and play that role. It tends to build new skills because you have to operate in different dimensions to land the right outcomes for the project.
Guide: Guides tend to be domain experts that are deeply involved in the architecture of a project. Guide will often drive the design but they’re not “The Architect.” A Guide often works through others to produce the designs, and themselves produce exemplary artifacts, like design docs or bodies of code. The code produced by a Guide is usually illustrative of a broader pattern or solving a difficult problem that the rest of the team will often run with afterwards. The difference between a Guide and a Sponsor is that the Guide focuses on the technical path for the project, and the Sponsor owns all aspects of project delivery, including product definition and organizational alignment.
Guides influence teams. If you are influencing individuals, you’re likely being a mentor and not a Guide. A Guide is a time-consuming role. You shouldn’t have time to Guide more than two projects, and that drops to one project if you are a Sponsor at the same time.
Catalyst: A Catalyst gets an idea off the ground, and it’s not always their idea. In my experience, the idea might not even come from the Catalyst—it can be something we’ve been talking about doing for years but never really got off the ground. Catalysts will create docs or prototypes and drive discussions with senior decision makers to think through the concept. Catalysts are not just “idea factories.” They take the time to develop the concept, drive buy-in for the idea, and work with the larger leadership team to assign engineers to deliver the project.
A Catalyst is a time-consuming role because of all the work that needs to be done. At Amazon, that involves prototypes, docs and discussions. It is hard to effectively Catalyze more than one or two things at once. It is important to note that Catalysts, like Tie-breakers, are not permanent roles. Once a project is catalyzed (e.g., in engineering with a dedicated team working on the project), a Catalyst moves out of the role. The Catalyst might take on a Guide or Sponsor role on the project, or not. Not every project needs a Catalyst. A Catalyst is a very helpful (arguably critical) role for your most ambitious, complex, and/or ambiguous problems to solve in the organization.
Tie Breaker: A Tie-Breaker makes a decision after a debate. At Amazon, that means deeply understanding the different positions, weighing in with a choice, and then formally closing it out with an email or a doc to the larger group. Not every project needs a Tie-Breaker. But if your project gets stuck in a consensus-seeking mode without making progress on hard decisions, a senior engineer might have to step in as a Tie-Breaker. Tie-breakers own breaking a log-jam on direction in the team by making a decision. Obviously, a Tie Breaker has to have great judgment. But, it is incredibly important that the Tie-Breaker listens well and understands all the nuances to the different positions as part of breaking the tie. When a Tie -Breaker drives a choice, they must bring other engineers into their thought process so that all the engineers in the debate understand the “why” behind the choice even if some are disappointed by the direction. A Tie-Breaker must have strong engineering and organizational acumen in this role.
Sometimes an organization will depend on a small set of senior engineers to play the role of Tie-Breaker because they are so good at it. As a successful Tie-Breaker, you want to be careful not to set a tone that every decision, no matter how small, must go through you. You’ll quickly transition from Tie-Breaker to a “decision bottleneck” at that point—and that is not a role any team needs. If a team finds itself frequently seeking out a Tie-Breaker, it could be a sign that the team needs help understanding how to make decisions. That’s a topic for a different time. The Tie-Breaker role is considered a “moment in time” role, versus Sponsor/Guide which are ongoing until you reach a milestone. Once the decision is made and closed out, you’re no longer the Tie-Breaker.
Catcher: A Catcher gets a project back on track, often from a technical perspective. It requires high judgement because a Catcher drives prioritization and formulating a pragmatic plan under tight deadlines. Catchers must quickly do their own detailed analysis to understand the nuances of the problem and come up with the path forward in the right timeframe. As a comparison, a Tie-breaker tends to step in when the pros/cons of the different approaches are well known and the team needs to make a hard decision. Once “caught” (i.e., the project is back on track and moving forward), a project doesn’t need the Catcher anymore.
Sometimes Principal Engineers can do too much catching. Don’t get me wrong, we are all Catchers sometimes—including me. Any fast-paced business needs Catchers in engineering and management. It teaches important skills about leadership in difficult moments and helps the business by landing deliverables. It also teaches you what not to do next time. However, it is better to generalize a Catcher skill set across more engineers and not depend on a small set to Principal Engineers as Catchers. If a Principal Engineer plays Catcher all the time through a succession of projects, it leaves no time to develop skills in other roles.
Participant: A participant works on something without one of these explicitly assigned leadership roles. A Participant can be active or passive. Active participants are hands-on, and do things like spend a few days working through a design discussion or picking up a coding task occasionally on a project, etc. Passive participants offer up a few points in a meeting and move on. In general, if you’re going to participate it’s better to do so actively. Time-boxing some passive participation (e.g., office hours for engineers) can be a useful mechanism to stay connected to the team. However, keep in mind that it is easy for your time to get consumed by being a Participant in too many things.
(via Marc Brooker)
Tags: roles principal-engineer work projects project-management amazon aws via:marc-brooker
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In my own experience as an artist, experimenting with AI has mixed results. I’ve used several “songwriting” AIs and similar “picture-making” AIs. I’m intrigued and bored at the same time: I find it quickly becomes quite tedious. I have a sort of inner dissatisfaction when I play with it, a little like the feeling I get from eating a lot of confectionery when I’m hungry. I suspect this is because the joy of art isn’t only the pleasure of an end result but also the experience of going through the process of having made it. When you go out for a walk it isn’t just (or even primarily) for the pleasure of reaching a destination, but for the process of doing the walking. For me, using AI all too often feels like I’m engaging in a socially useless process, in which I learn almost nothing and then pass on my non-learning to others. It’s like getting the postcard instead of the holiday. […]
All that said, I do believe that AI tools can be very useful to an artist in making it possible to devise systems that see patterns in what you are making and drawing them to your attention, being able to nudge you into territory that is unfamiliar and yet interestingly connected. I say this having had some good experiences in my own (pre-AI) experiments with Markov chain generators and various crude randomizing procedures. […]
To make anything surprising and beautiful using AI you need to prepare your prompts extremely carefully, studiously closing off all the yawning, magnetic chasms of Hallmark mediocrity. If you don’t want to get moon rhyming with June, you have to give explicit instructions like, “Don’t rhyme moon with June!” And then, at the other end of the process, you need to rigorously filter the results. Now and again, something unexpected emerges. But even with that effort, why would a system whose primary programming is telling it to take the next most probable step produce surprising results? The surprise is primarily the speed and the volume, not the content.
Tags: play process technology culture future art music ai brian-eno creation
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This, like so much of the Pimoroni catalog, is a lovely piece of gadgetry. If I hadn’t already built a very nice e-ink home dashboard a couple of years back, I would definitely be doing so using one of these.
Honorable mention goes to the Pimoroni Presto: https://shop.pimoroni.com/products/presto?variant=54894104019323 , which is a beautifully designed tiny colour touchscreen with an RP2350 onboard. Not e-ink, unfortunately, which is a key feature for pervasive dashboards IMO, but still, I can see lots of use-cases for that gadget too….
Tags: e-ink gadgets dashboards home devices hardware pimoroni rp2350 hacks
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Here we go, with another predictive algorithm-driven bias machine used to drive refusal of benefits:
Lighthouse Reports and Svenska Dagbladet obtained an unpublished dataset containing thousands of applicants to Sweden’s temporary child support scheme, which supports parents taking care of sick children. Each of them had been flagged as suspicious by a predictive algorithm deployed by the Social Insurance Agency. Analysis of the dataset revealed that the agency’s fraud prediction algorithm discriminated against women, migrants, low-income earners and people without a university education. Months of reporting — including conversations with confidential sources — demonstrate how the agency has deployed these systems without scrutiny despite objections from regulatory authorities and even its own data protection officer.
Tags: sweden predictive algorithms surveillance welfare benefits bias data-protection fraud
Thalidomide chirality paradox explained
Molecule chirality (“left-handedness” and “right-handedness”) has been in the news again recently.
What is little known is the relevance of chirality to the thalidomide disaster. Thalidomide, the drug which was prescribed widely to pregnant women in the 1950s for the treatment of morning sickness, was later discovered to be a chiral molecule, and while the left-handed molecule was effective, the right-handed one was extremely toxic, causing thousands of children around the world to be born with severe birth defects. The mystery is, why didn’t this toxicity emerge during animal experiments? Here’s a paper with a potential explanation:
Twenty years after the thalidomide disaster in the late 1950s, Blaschke et al. reported that only the (S)-enantiomer of thalidomide is teratogenic [jm: causing birth defects]. However, other work has shown that the enantiomers [“mirror” molecules] of thalidomide interconvert in vivo, which begs the question: why is teratogen activity not observed in animal experiments that use (R)-thalidomide given the ready in vivo racemization (“thalidomide paradox”)? Herein, we disclose a hypothesis to explain this “thalidomide paradox” through the in-vivo self-disproportionation of enantiomers. Upon stirring a 20% ee solution of thalidomide in a given solvent, significant enantiomeric enrichment of up to 98% ee was observed reproducibly in solution. We hypothesize that a fraction of thalidomide enantiomers epimerizes in vivo, followed by precipitation of racemic [equally mixed between R/S forms] thalidomide in (R/S)-heterodimeric form. Thus, racemic thalidomide is most likely removed from biological processes upon racemic precipitation in (R/S)-heterodimeric form. On the other hand, enantiomerically pure thalidomide remains in solution, affording the observed biological experimental results: the (S)-enantiomer is teratogenic, while the (R)-enantiomer is not.
Tags: chirality thalidomide molecules drugs medicine papers chemistry
UK passes the Online Safety Act
Apparently “The Online Safety Act applies to every service which handles user-generated content and has “links to the UK”, with a few limited exceptions listed below. The scope is extraterritorial (like the GDPR) so even sites entirely operated outside the UK are in scope if they are considered to have “links to the UK”.”
A service has links to the UK if any of the following apply: – the service has a “significant number” of UK users – UK users form one of the target markets for the service – the service is accessible to UK users and “there are reasonable grounds to believe that there is a material risk of significant harm to individuals in the UK” (this seems less likely to apply for smaller services but who knows)
Tags: osa uk safety regulations ofcom
Why did Silicon Valley turn right?
A great essay on the demise of the 1990s/2000s liberal consensus in Silicon Valley:
No-one now believes – or pretends to believe – that Silicon Valley is going to connect the world, ushering in an age of peace, harmony and likes across nations. […] A decade ago, liberals, liberaltarians and straight libertarians could readily enthuse about “liberation technologies” and Twitter revolutions in which nimble pro-democracy dissidents would use the Internet to out-maneuver sluggish governments. Technological innovation and liberal freedoms seemed to go hand in hand. Now they don’t. Authoritarian governments have turned out to be quite adept for the time being, not just at suppressing dissidence but at using these technologies for their own purposes. Platforms like Facebook have been used to mobilize ethnic violence around the world, with minimal pushback from the platform’s moderation systems […] My surmise is that this shift in beliefs has undermined the core ideas that held the Silicon Valley coalition together. Specifically, it has broken the previously ‘obvious’ intimate relationship between innovation and liberalism. I don’t see anyone arguing that Silicon Valley innovation is the best way of spreading liberal democratic awesome around the world any more, or for keeping it up and running at home. Instead, I see a variety of arguments for the unbridled benefits of innovation, regardless of its benefits for democratic liberalism. I see a lot of arguments that AI innovation in particular is about to propel us into an incredible new world of human possibilities, provided that it isn’t restrained by DEI, ESG and other such nonsense. Others (or the same people) argue that we need to innovate, innovate, innovate because we are caught in a technological arms race with China, and if we lose, we’re toast. Others (sotto or brutto voce; again, sometimes the same people) – contend innovation isn’t really possible in a world of democratic restraint, and we need new forms of corporate authoritarianism with a side helping of exit, to allow the kinds of advances we really need to transform the world.
Tags: essays henry-farrell tech politics silicon-valley fascism democracy liberalism
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How a simple math error sparked a panic about toxic chemicals in black plastic kitchen utensils:
Plastics rarely make news like this. From Newsmax to Food and Wine, and from the Daily Mail to CNN, the media uptake was enthusiastic on a paper published in October in the peer-reviewed journal Chemosphere. “Your cool black kitchenware could be slowly poisoning you, study says. Here’s what to do,” said the LA Times. “Yes, throw out your black spatula,” said the San Francisco Chronicle. Salon was most blunt: “Your favorite spatula could kill you,” it said. [….] The paper correctly gives the reference dose for BDE-209 as 7,000 nanograms per kilogram of body weight per day, but calculates this into a limit for a 60-kilogram adult of 42,000 nanograms per day. So, as the paper claims, the estimated actual exposure from kitchen utensils of 34,700 nanograms per day is more than 80 per cent of the EPA limit of 42,000. That sounds bad. But 60 times 7,000 is not 42,000. It is 420,000. This is what Joe Schwarcz [director of McGill University’s Office for Science and Society] noticed. The estimated exposure is not even a tenth of the reference dose.
(tags: cooking research science plastics errors maths math fail papers)