Seth of the Fediverse ⁂

Seth of the Fediverse ⁂

@phillycodehound.indieweb.social.ap.brid.gy

I'm a Techie, #OpenWebAdvocate, #DigitalMarketer, and I love #WordPress. I'm also a former professional #journalist. I write the #MarketingJunto #newsletter […] πŸŒ‰ bridged from ⁂ https://indieweb.social/@phillycodehound, follow @ap.brid.gy to interact

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Seth of the Fediverse ⁂ reposted

1/ On the latest Entrepreneur's Enigma, Jon Dyer of Artisan Painting shares why top-notch customer serviceβ€”picked up from his restaurant daysβ€”is the real secret sauce for painting success!

Seth of the Fediverse ⁂ reposted

For Jon Dyer, entrepreneurship means a lot of things. But one thing he values the most is making sure is that everyone at Artisan Painting has a work/life balance. This is a core principle for him. #founderstory #worklifebalance #entrepreneurship

Seth of the Fediverse ⁂ reposted

It's Monday again, and it's nearly time for This Week in WordPress. Join us LIVE at 2pm UK time (about 1 hour). See you there? https://wpbuilds.com/live Join Nathan Wrigley, Michelle Frechette, Ivana Ćirković, Dave Grey.

wpbuilds.comwpbuilds.comFind out what’s coming up… Skip Calendar Choose View Month - Classic Month - List Week - Time Grid Week - List Week - Day Grid Day - List List - Upcoming Month - List with Image Back To Full Month < > Event Title * * * Starts Ends * * * View Add To Calendar Event times are listed in your local time zone: Go to our YouTube channel You should be able to access LIVE chat from YouTube, so you’ll need to be logged into a Google / YouTube account for that. Alternatively, once we’re live, there’s a β€˜Live Chat’ icon at the top-right of the video which does not require you to be logged in anywhere. Open the chat in a new tab YouTube Comments ### Would you like to share this... * Share on Bluesky (Opens in new window) Bluesky * Share on Mastodon (Opens in new window) Mastodon * Share on X (Opens in new window) X * Share on Threads (Opens in new window) Threads * Share on LinkedIn (Opens in new window) LinkedIn * Share on Facebook (Opens in new window) Facebook * Share on Telegram (Opens in new window) Telegram * Share on WhatsApp (Opens in new window) WhatsApp * Share on Reddit (Opens in new window) Reddit * Share on Tumblr (Opens in new window) Tumblr * Email a link to a friend (Opens in new window) Email * Print (Opens in new window) Print * Share on Pinterest (Opens in new window) Pinterest * ### Like this: Like Loading…

Seth of the Fediverse ⁂ reposted

so this also included android tablets, and all dozen of us are v thankful for it 🫢🏼

Bluesky@bsky.app

Also new in today’s v1.136 update: iPad support! One of our most-requested features is finally here in beta. Try out the Bluesky app on your iPad, and if you see anything off, let us know under Settings > Beta features.

Seth of the Fediverse ⁂ reposted

Good AIrticle by @pluralistic in particular the distinction between "good AI" and "bad AI" and actual numbers behind the AI bubble, ie. investment vs returns. Read it! https://locusmag.com/feature/commentary-cory-doctorow-technopolitics/

locusmag.comCommentary: Cory Doctorow: TechnopoliticsCory Doctorow (Copyright Julia Galdo & Cody Cloud) It’s one thing to be β€œanti-AI” in the sense of believing that AI-generated slop, disinformation and deepfakes are bad; that AI companies that hope to con your boss into firing you and replacing you with AI that can’t do your job are bad; that betting the economy on AI is bad; that betting the human-habitable planet Earth on AI is bad; that running roughshod over local environmental rules and seizing farmland for data centers is bad. Those are all good reasons to be β€œanti-AI.” But being β€œanti-AI” in the sense of β€œopposing the statistical inference techniques we call AI” is weird. So is being anti-AI in the sense of β€œopposing the use of models that were created using large language corpuses because it’s inherently wrong to compute the frequency with which words occur in writΒ­ten language.” And it’s also weird to be anti-AI in the sense of β€œopposing AI because someone, somewhere used AI in a way that was gross, harmful or exploitative.” Today, the two kinds of β€œanti-AI” are mostly a distinction without a difference, because nearly all the world’s AI output comes from one of the β€œfrontier models” made by a giant AI company. No matter why you oppose AI, you probably don’t like those frontier models. But after the AI bubble bursts, the distinction will be much sharper. Long after the collapse of the seven AI companies that make up 35% of the S&P 500 stop trading the same $100 billion IOU around really fast while pretending it’s in all their bank accounts at once, there will still be cheap bankruptcy-sale hardware, legions of unemployed skilled AI workers, and the open-source models released by today’s AI giants will still run, and will continue to be improved by the people who rely on them. The useful things AI does will keep happening. You’ll still be able to prompt up a single-serving, personal piece of software to perform one task with moderate reliability for your use, and when it stops working, you’ll be able to do it again. You’ll still be able to transcribe audio, or summarize a document. You’ll still be able to use a chatbot as an interactive journal, or as a supercharged spellchecker. None of that will change – and neither will the limits on all those activities. If you don’t know enough about a document or its subject to spot obvious errors in an AI summary, you’ll be periodically misled. If you’re so desperate or uninformed that you trust summaries you lack the capacity to evaluate in critical situations, you may be led to disaster. If you don’t know anything about computers and you turn a piece of vibe-coded software loose on your machine, you might delete something important, or irreversibly expose sensitive data to the public internet. AI isn’t exceptional, it’s normal. It’s not exceptionally dangerous. It’s not exceptionally evil. It’s also not exceptionally useful or exceptionally good. AI represents a significant advance to computer science – a moderate variation to existing machine learning techniques that generated a much larger improvement in those techniques. In the absence of the AI investment bubble, we’d call the things AI can do β€œplug-ins” – as in, β€œI just got a new plug-in for my word processor that generates plausible word salads. I can see how that might be useful for generating placeholder text, and I can tell that a lot of people are going to use it in ways I find supremely annoying.” I’ve had a lot of plug-ins for my word processors over the past 40 years, and most of them fit that description. None of them ever made me want to bet the global economy on it, or to feed all the writers into a woodchipper. AI can do many useful things. Some of them are _very_ useful: Friends who work in advanced statistics tell me that AI coding assistants help them produce high-quality, reliable code that solves real problems, improving the quality of the work they do. I find AI-generated transcripts of audio very useful for locating quotes in podcasts I’ve recently listened to, and I use a chatbot that runs on my computer to check my essays for typos. This bot often catches typos that my text editor’s checker misses, among a smattering of false positives that I have the experience and discernment to disregard (you know, like every spell checker). But AI doesn’t exist in the absence of the bubble – it very much exists in the white-hot center of the most dangerous investment bubble in the history of markets. A year ago, the AI sector’s total expenditures were about $700b. Today, they’re over $1.4t, and Gartner projects that will rise to $2.4t by the end of the year. This is an astonishing sum of money to commit to a sector that grosses a mere $50b worldwide, annually. The sector’s failure to produce anywhere near as much value as it is consuming has not dimmed its backers’ ardor, and they are committed to spending, and spending, and spending, until either they run out of investors or they somehow transform $50b worth of revenue on $2.4t in expenditures into profit. To this end, the sector’s masters are committing all manner of unforgivable sins. These range from conning your boss into firing you and replacing you with an AI that can’t do your job; to convincing your town council to seize your home and build a data center, without any environmental review or public input; to swindling everyday savers with nonsensical IPOs and sweetheart deals with stock exchanges that let insiders cash out their stupid AI bets with your retirement savings. These sins are baked into this kind of AI production. There is no way to raise trillions in subsidies that allow AI companies to sell computation at a subsidy that amounts to selling $100 bills for $1 each without also insisting that the end state of all of this is that every job will be swallowed by a chatbot. You can’t raise trillions on this promise withΒ­out β€œblitzscaled” data-centers – accompanied by climate-shredding gas turbines. You can’t raise trillions without cramming AI into every part of every person’s digital life, without any safeguards for how vulnerable people might use them to sink deep into dangerous delusion. These are the β€œtechnopolitics” of AI: To make this AI, the people inΒ­volved _had_ to inflict all these harms upon us. The important questions are: to what extent are these the _necessary_ technopolitics of AI, and; can those technopolitics change? Langdon Winner’s 1980 article, β€œDo Artifacts Have Politics?” is the Ur-text for questions like these. In this seminal essay, the rock critic turned tech scholar Winner demands that we move beyond the simple framing of tech having β€œintended” and β€œunintended” consequences, and instead insists that politics come baked into some technologies. Winner divides the politics of technology into two broad categories: politics that are derived from the specific way that a technology has been introduced into the world; and politics that are either necessitated by, or strongly compatible with a technology by dint of its technical charΒ­acteristics. For the first category – objects whose politics are determined by deΒ­ployment decisions – Winner cites a set of notorious Long Island freeway overpasses. These were specified by Robert Moses, and in _The Power Broker_ , Robert A. Caro’s seminal biography of Moses, we learn that Moses delibΒ­erately chose to make these overpasses too low for a bus to pass beneath, in order to prevent a public transit route from being established between New York’s Black neighborhoods and Long Island’s public beaches, favored by the white bourgeoisie. According to Winner, a freeway overpass isn’t necessarily political, but these overpasses are. What’s more, their politics is durable, outlasting Robert Moses’s tenure as New York’s most powerful political figure, even outliving Moses himself. For the second category – technologies whose politics emerge from their intrinsic technical characteristics – Winner compares solar and nuclear power. Nuclear reactors, he argues, require a central authority to build, operate, and maintain (we might add here that they also require a conΒ­stant stream of fissiles that can be extracted only from certain unevenly distributed mining sites). By contrast, solar can be installed anywhere the sun shines, established and overseen by small, decentralized groups. For Winner, nukes are irreducibly the tech of large, centralized states; while solar is potentially the tech of autonomous, nonhierarchical societies. Between these two technological poles, Winner discusses a third exΒ­ample: a tomato harvester. The harvester enabled farmers to fire many of their pickers, giving power to bosses at the expense of workers. At the same time, the harvester only worked with hardier varietals that traded flavor for durability, yielding a cheaper, worse kind of tomato that drove out competing tomatoes that tasted better but cost more to raise because their cultivation wasn’t as amenable to cost-saving automation. The tomato harvester wasn’t created to weaken labor and worsen tomatoes, but that was its effect, and so the politics of this harvester included a certain kind of farm, a certain kind of labor relation, and a certain kind of foodway. AI certainly has technopolitics. At a miniΒ­mum, AI has the technopolitics of the first kind, those Robert Moses bridges. It has been capitalized specifically on the basis that it can be used to erode workers’ wages and labor power and to foist inferior goods on the public. There is no other way that the AI sector’s unimaginably vast capital losses can be turned into profits. The sums involved are so large that virtually everything everyone does, all the time, will need to be replaced with AI, and so it is also in AI’s technopolitics that AI must be crammed into every activity we undertake, whether or not it belongs there, and whether or not it presents a danger to the user, or to the people who depend on the user’s work (think here of cramming AI into automated systems for generating criminal accusations, which then prompt the police to make false arrests). Now, none of this is intrinsic to the technoloΒ­gy. Remember, the technology we’ve been calling β€œAI” for the past decade is a set of small variaΒ­tions on existing machine learning techniques that yielded surprisingly large dividends relative to the size of those variations. This improvement was scalable – meaning that when computer scientists pushed these variations harder, they got concomitantly outsized improvements in the machine learning systems’ performance. This is one of the best kinds of technological breakthrough to be in the midst of, one where you discover a piece of low-hanging fruit that turns out to be at the edge of a whole _orchard_ full of similarly low-hanging fruits. There’s nothing _technical_ about the discovery of this variation on machine learning that dicΒ­tates the current AI approach. In an alternate universe, β€œAI” could be a $100b joint supercomΒ­puting effort between the International Panel on Climate Change and Sandia National Labs, used to improve the fidelity of our climate models. This, too, would produce carbon emissions, but not nearly as many, and of course, everything people do has an impact on the environment (the only way to stop impacting the environment is to a) die; and b) decompose). This alternate uniΒ­verse might also see a proliferation of hobbyist open source / open weight models spinning out of these efforts, with online forums and social media filled with generated text and sounds and images. As with every technology controlled by its users, some users will find ways to sin against the law and ethics. There will be some gross stuff around the edge (voice cloning for fraud, deepfake porn, etc), but for the public, the main AI event would be a bunch of plug-ins that were byproducts of a big public research project. The crimes and sins of AI will become a matter for social and legal control – not problems that we can only solve if we can convince vast, corrupt, wealthy corporations to take action. Could we have this AI future still? Well, after the AI bubble pops, we’re going to have _something_. We’re going to have the open-source models that the hyperscalers released as demos for their main event β€œfrontier models.” We’ll have bankruptcy-sale hardware. We’ll have an army of skilled workers who will no longer be compelled by their bosses to try and cram AI into all of our orifices or to convince our bosses to fire us and replace us with chatbots that can’t actually do our jobs. The question, though, is whether AI is more like a Robert Moses overpass, a nuclear reactor, or a tomato harvester? Are there _inescapable_ aspects of the current generation of AI models that mean that using them independently of the hyperscalers will still somehow perpetuate the harms of the current AI bubble? One argument for this is that the fruit of the poisoned tree is forever poisonous. The fact that these AI tools were made by bad people for bad reasons makes everything you do with the tools bad. The most extreme version of this argument is that it’s the mere fact of a sinful origin that disqualifies the hyperscalers’ open AI models from moral consideration. This is a bad argument. There’s hardly any technology – any built infrastructure – that is free from sin. Every microchip you use is filled with billions of linear descendants of William Shockley’s silicon transistors. Shockley was a monster: a virulent racist and eugenicist who spent his Nobel Prize bounty in efforts to pay Black women to have their tubes tied as part of a disgusting racist project. Going further back: Europe’s roads owe their existence to Rome’s imperial conquest, a bloody campaign that included mass enslavement and the indiscriminate slaughter of civilians. EuΒ­rope’s modern roads aren’t necessarily any betΒ­ter: Hitler himself turned over the first spadeful of dirt for Austria’s Autobahn. It’s important to remember these facts and to make reparations for old crimes – but just as you can’t both be alive and avoid impacting the environment, you also can’t be both alive and free from contact with the relics of monsters both modern and ancient. Anyone who claims that they live a life that isn’t steeped in blood both old and fresh is kidding themselves. By all means, do everything you can to right wrongs, but if you only permit yourself to act in ways that avoid contact with the product of sin, you won’t do _anything_ , let alone anything to right wrongs. Imagine a new generation of buses could handily fit under Robert Moses’s Long Island overpasses, such that the overpasses could no longer be used to racially and economically segregate the city’s beaches. At that point, the overpasses’ technopolitics change. They may have been made for an evil purpose, but now that they no longer serve that purpose, they’re just bridges. The mere fact of their historical sin is no reason to tear them down today. A less extreme version of the sinful origins arΒ­gument is that the ongoing use of a sin-steeped object is the source of fresh evil in the world. Museums that display the looted remains of indigenous people don’t merely dishonor those long-dead people: They inflict ongoing harm on living indigenous people whose elders are reduced to colonial curiosities, making indigΒ­eniety itself a relic, as opposed to a living force in the world that is critical to the lives and thriving of millions of people. I think a version of this can be applied to some of the hyperscalers’ models. Models that have been trained on – and retain – the ill-gotten private information of millions of people are indeed so wicked that they should be deleted. Just as the mere existence of pornographic images of children is an ongoing harm to the people who were abused to create them, the mere existence of databases of ill-gotten private information is an ongoing source of harm and risk to the people in the database. If there’s a chance that an AI model can be made to yield up your personal, sensitive information, we should destroy it. But if there are databases – models – that were trained on private information, _in such a way that they no longer retain that information_ , then they are more like the Autobahn than they are like a trove of child sex abuse material. They are steeped in sin, they should not have been made in the way they were, but the ongoing use of them causes no new harm to anyone, and so if they are useful, we should use them – even as we erect protections to prevent anyone else from engaging in mass privacy invasions for any purpose, including AI, ever again. Think here of the thousands of miles of fiber-optic cables that Worldcom buried under the streets of cities all over the world. Worldcom was a massive, wicked, fraudulent enterprise, whose executives lied to the public in order to steal billions from everyday working people, leaving them bankrupt or separated from their retirement savings and doomed to a miserable old age. For this crime, Worldcom CEO Bernie Ebbers was sentenced to prison, and he died there. And yet…the fiber is still under the ground. It got there by the most disgusting, harmful means imaginable. But treating those cables as morally untouchable doesn’t help any of those bankrupt pensioners. At my home in Los Angeles, I have a 2GB symmetrical fiber link from AT&T, who bought and lit up some of Worldcom’s fraudulent, pensioner-destroying fiber. I would rather live in a world where I had slower internet and the pensioners still had their money, but that’s not an option. At this point, the fiber is just fiber – its use doesn’t harm anyone anew, nor would refusing to use it bring any benefit to anyone. Some people’s anger over AI models’ training data is couched in claims about copyright, not privacy. The argument goes that by making transient copies of billions of public works and then tabulating the relationship of the elements in them, the AI companies are stealing from creative workers. I think this represents dangerΒ­ously misdirected anger. It’s totally legitimate to be angry at a tech executive with billions in investor capital who publicly announces their intention to destroy your livelihood – even moreso if they use your own creative output to accomplish this end. But the bad conduct here lies in the intenΒ­tion and action to destroy your livelihood. It does _not_ lie in making copies of your work in order to count the words or the pixels in them. Copying and analyzing every public work is how every search engine works. What’s more, this practice wasn’t invented by search engines. Long before the first digital computers, the Oxford English Dictionary examined the entire corpus of publicly available written work to trace the etymology of our language. Long before Google News, the library where I worked subscribed to a quarterly, hardbound subject index of all the articles published in every general-interest English-language newspaper and magazine. A language model is a collection of embedded inferences that certain words are likely to follow from other words. You may rightfully regard the use of a model trained on your work to attack your wages as unethical, but that’s about the model’s _operation_ , not its _creation_. The fact that the model has derived some of these inferences by counting all the words you ever published is not theft – no more than it’s theft for the OED to identify the first times I used the word β€œenshitΒ­tification” in its entry on my word. Counting words – even all the words ever uttered – isn’t theft. We call that β€œa search engine.” If an open model, operated and hosted by someone other than the hyperscaler that shipped it, is sold or used to destroy your liveliΒ­hood, then the person using that model is bad, and we should have social, economic and legal sanctions for that person. But the issue here is the _use_ of the model. A neofascist who uses the Autobahn to travel to a racist rally is a Nazi – but they’re a Nazi because of what they’re doing, not because they’re doing it on Hitler’s highway. The technopolitics of language models are bad because the people selling language models market them for the purpose of bankrupting writers and filling the world with slop. But there’s nothing intrinsic to language models that decrees their use in this fashion. The computer scientist Pete Warden has a lovely demo of a language model running on a sub-$10 system on a chip (SOC) that can ingest and conversationally access the manual for an apΒ­pliance. In his demo, Warden uses this system to create an interactive dishwasher manual: tap a button on the dishwasher and ask it a question like β€œWhat should I do if I’m getting streaks on my glassware?” and it will locate the correct answer in the manual and tell you what to try. This language model may have been trained on the corpus of all available literature without permission, and the company that did the training may have raised their capital on the promise of bankrupting writers with their own words, but the button on your dishwasher that uses an open, disconnected model to give you advice on getting your dishes clean in no way perpetuates those harms. If tomorrow someone invented a nuclear reactor that sifted uranium out of seawater and ran safely in your garage, it wouldn’t have the same centralizing technopolitics as the nukes that Winner concerned himself with – and that would be true even if the factory that produced these decentralized pocket reactors ran on a ten billion dollar nuke filled with traditional enriched uranium. The technopolitics aren’t tied to the fact that the nuke produces energy by splitting atoms – it’s tied to the technical reality of the only way we know to accomplish that feat. If we learn new atom-splitting techniques, they can have different technopolitics. By the same token, a language model – even one released by a despicable Altman or Amodei – has embedded technopolitics because of the kinds of infrastructure it requires, not because it guesses words. Guessing words is fine. It’s someΒ­times useful. The open models that OpenAI and Anthropic have put into the world and can never claw back will be guessing words long after those companies are dust. We can use those open models for good or ill – we can choose their technopolitics. Technopolitics are a matter of what happens when you use a technology, and what you have to do to maintain and improve that technology. Optimizing open models is a matter for collaborative, open effort – not closed labs fueled by investors’ centibillions. AI _has_ technopolitics. But those politics are neither inevitable nor immutable. Sam AltΒ­man’s lurking presence in a language model is no more politically disqualifying than William Shockley’s residue in your microchips. AI today might be a defective tomato harΒ­vester, immiserating workers and driving out good tomatoes with bad. But there’s nothing intrinsic in the idea of a tool for harvesting a tomato that is bad for workers – or tomatoes. It’s because we let bosses choose which tomato harvester was suitable for use that we ended up in this place. Put the pickers in charge of that decision and we’d have very different harvesting technology – and far more delicious tomatoes. * * * #### Cory Doctorow CORY DOCTOROW (craphound.com) is a science fiction author, activist, and journalist. He is the author of dozens of books, most recently Enshittification: Why Everything Suddenly Got Worse And What To Do About It (nonfiction); and the novels Picks And Shovels and The Bezzle (followups to Red Team Blues). Other notable books include the solarpunk novels Walkaway and The Lost Cause; the tech policy books The Internet Con and Chokepoint Capitalism; the internationally bestselling YA Little Brother series; and the picture book Poesy The Monster Slayer. He maintains a daily blog at Pluralistic.net. He works for the Electronic Frontier Foundation, is an AD White Professor at Cornell University; an MIT Media Lab Research Affiliate; a Visiting Professor of Computer Science at Open University. He co-founded the UK Open Rights Group. In 2020, he was inducted into the Canadian Science Fiction and Fantasy Hall of Fame. All opinions expressed by commentators are solely their own and do not reflect the opinions of Locus **While you're here, please take a moment to support _Locus_ with a one-time or recurring donation. We rely on reader donations to keep the magazine and site going. We publish hundreds of reviews and news stories each year, but we can't do it without your help. ** Β©Locus Magazine. Copyrighted material may not be republished without permission of LSFF.

Seth of the Fediverse ⁂ reposted

Group functionality continues to mature for Bonfire Community, so I recommend that everyone ought to check out a group to join! I ended up joining Fediverse Memes from Feddit, but any group from Lemmy, NodeBB, MBin, or PieFed ought to work! fedizen.net/groups

fedizen.netGroups Β· FedizenFedizen is an experimental Bonfire instance being built specifically to aid network-wide discovery for all things Fediverse-related. Brought to you by the people at We Distribute!

Seth of the Fediverse ⁂ reposted

Doug Downs of Stories & Strategies is my guest tomorrow on Entrepreneur's Enigma. It was such a fun discussion. Be sure to tune in tomorrow morning to listen to two podcast nerds talk podcast growth and so much more! #PodcastGrowth #B2BPodcasting #PodcastProduction #PodcastGoals

Seth of the Fediverse ⁂ reposted

1/ On the latest Entrepreneur's Enigma, Seth Goldstein chats with serial entrepreneur David Salerno! From launching a packaging biz in Montreal to coaching founders across North America, his journey is seriously inspiring.

Seth of the Fediverse ⁂ reposted

"We have to be talking about what we joyfully running towards, not what we fearfully backing away from." Anil Dash in his #FediForum keynote this week.

Seth of the Fediverse ⁂ reposted

Most people don't do accessibility for disabled people. This is just a fact. But developers love to cling on technology: "Let's do stuff for screen readers!" "Here is a widget for making stuff more accessible." If you mechanize accessibility, people are interested. That's where the whole "do […]

yatil.socialOriginal post on yatil.social

Seth of the Fediverse ⁂ reposted

"historical fascism’s lust for violence didn’t make it repulsive but extremely attractive to a lot of German voters. And we are seeing similar things with the AfD today, where fantasies of violent retribution seem to be integral to campaign events on market squares." ... And not only Germany

theguardian.comβ€˜Hitler was a lazy criminal who slept in until noon’: why most of what we think we know about the rise of the Nazis is wrongIn a new book, German artist and writer Philipp Ruch argues that viewing the ascent of Hitler’s NSDAP through the prism of the Third Reich has distorted our understanding of fascism’s electoral breakt...