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After 25 years, Wikipedia has proved that news doesn't need to look like news (niemanlab.org)
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Oh goodness, if wiki is news, then it's the most biased and easily editable news outside of Winston Smith and the Ministry of Truth.
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If you're reading Wikipedia as the news without skepticism, you're idiot
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>just about every link to a Wikipedia page created in the past quarter-century still works Not so sure about this; page titles change and redirects get removed. I'm thinking of https://en.wikipedia.org/wiki/Death_of_Nex_Benedict where initial news articles and her obituary used her birth name, Dagny Benedict, but soon this name was scrubbed from the wikipedia page, as well as its talk page and redirects, on the policy of deadnames.
after 25 years wikipedia showed what it truly was created for, by selling the content for training. otherwise - okay, this was a cool project, perhaps we need better. like federated, crypto-signed articles that once collected together, @atproto style, produce the article with notable changes to it.
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Context: https://arstechnica.com/ai/2026/01/wikipedia-will-share-content-with-ai-firms-in-new-licensing-deals/ tl;dr: Wikipedia is CC and has public APIs, but AI companies have recently started paying for "enterprise" high-speed access. Notably, the enterprise program started in 2021 and Google has been paying since 2022.
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Their enterprise offering is more for fresh retrieval than training. For training, you can just download the free database dump — one you would inadvertently end up recreating if you were to use their enterprise APIs in a (pre-)training pipeline.
US electricity demand surged in 2025 – solar handled 61% of it (electrek.co)
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Jevons paradox in action https://en.wikipedia.org/wiki/Jevons_paradox
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Also known as induced demand (as more is available)
Solar can be deployed by hundreds of thousands of individual efforts and financing at the same time, with almost no bureaucracy. It starts to produce electricity basically the same day. I can't imagine anything being able to compete with that for speed and scale - or costs, for that matter. Once deployed it's basically free.
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The issue is that works perfectly well when solar is a small % of the grid, but when that number grows, then you need grid scale solutions and coordination for things to continue working well. And that requires both technical skill and political will.
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We see that quite often here in the summer as the energy price sometimes drops to minus 60ct/kWh (more often it hovers around -5 to -10). It is pretty much "please use everything now" to avoid grid issues. It often happens on very clear days with lots of wind.
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(Home) batteries are quickly becoming cheap and per-hour electricity rates can be implemented at a reasonable time. With that, the grid owner can influence the grid stability without having to build capacity or generation itself.
Curiously, TFA doesn't raise the question of why demand surged, it spends its 8 microparagraphs only praising solar.
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I'm going to go out on a limb and say it has some thing to do with those data centers and LLM stuff.
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Funny, I was thinking the same thing.
Map To Poster – Create Art of your favourite city (github.com/originalankur)
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I tried it in a python3 venv, but the download data step is stuck at 0% unfortunately.
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It will work, give it time. Also default distance is 29000. Give distance of 10000 to see faster results. It certainly is working as many users have generated maps.
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Also note that as per their terms and conditions I need to give a user agent, so multiple users will be sharing same user agent right now.
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The map of Venice seems to be the only one whose image is "squeezed" horizontally. Wondering why.
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need to take into consideration the length and width of the city and choose distance appropriately. I haven't followed my own suggestion here - https://github.com/originalankur/maptoposter?tab=readme-ov-file#distance-guide will fix.
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That's splendid. I've long wanted to make a jigsaw puzzle out of Sydney's road map, so I can familiarise myself with the layout of roads while having fun. That way I can reduce my reliance on nav app and become one of those old-school drivers.
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There's a reason Where 2 -> Google Maps happened in Sydney. The sheer number of one-way roads combined with the imposition of the harbour and the messy tunnel system make internalising Sydney navigation a life-long endeavour
ClickHouse Acquires Langfuse (langfuse.com)
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I predict it will be Pydantic next to get picked up by someone for logfire and agent framework.... fine as long as all these open source projects stay open source then good for them
Congratulations to everyone involved, quite remarkable considering Langfuse was only founded as part of YC 23.
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Without the purchase price, it is unclear whether this deserves congratulations or condolences. Two years in the LLM race will have definitely depleted their seed raise of $4m from 2023, and with no news of additional funds raised it's more than likely this was a fire sale.
they want to enter the llm observability market and langfuse has already built a convenient wrapper around clickhouse that companies have adopted https://clickhouse.com/blog/clickhouse-raises-400-million-series-d-acquires-langfuse-launches-postgres#llm-observability-clickhouse-enters-the-market-with-langfuse-acquisition
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thank you! i missed that news
Show HN: Streaming gigabyte medical images from S3 without downloading them (github.com/PABannier)
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https://dicom.nema.org/dicom/dicomwsi/ Interesting guide to the Whole Slide Images (WSI) format. The surprising thing for me is that compression is used, and they note does not affect use in diagnostics. Back in the day we used TIFF for a similar application (X-ray detector images).
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How does this handle images with different compression formats?
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This is really a job for JPEG-XL, which supports decode of portions of larger images and has recently been added to the DICOM standard.
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Interesting - I'm not so familiar with S3 but I wonder if this would work for WSI stored on-premises. Imposing lower network requirememts and a lightweight web viewer is very advantageous in this use case. I'll have to try it out!
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When WSI are stored on-premise, they are typically stored on hard drives with a filesystem. If you have a filesystem, you can use OpenSlide, and use a viewer like OpenSeaDragon to visualize the slide. WSIStreamer is relevant for storage systems without a filesystem. In this case, OpenSlide cannot work (it needs to seek and open the file).
You have three minutes to escape the perpetual underclass – geohot (geohot.github.io)
geohot's not a regular joe, he's founded multiple companies and is a leader in our community. This is like a general being like "why are you letting the enemy win?" while he sits comfortably in his study managing his cigar collection.
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I still don't think it precludes him from having this opinion. Could he be doing more? Sure, but having found success in this system doesn't make his criticisms of it invalid.
> It is the input that continuously reconstitutes itself around whatever remains scarce, valuable and socially demanded as productivity rises. Even if, AI is going to tank the bargaining power of the working class even harder than it already is. It's already the reality for many that they're working for minimum wage, in toxic environments, with no benefits, and more overtime than legal in places that regulate it solely because they have very little better choice. Furthermore, this power inequality directly translates to influence over economic output of our civilization - by the time value of human menial or cognitive labor goes low enough to delete jobs, all that will be left will be various equivalents of being a sugar baby for the rich - fulfilling their emotional, sexual, and social needs. Not even art, because that's among the things gen AI is displacing the most effectively. > Middle class status anxiety manifesting as a rhetoric about neofeudalism. The middle class is a tiny, tiny fraction of the population nowadays. Even among those working high-earning jobs like tech/healthcare/finance, most are just upper worker class.
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> Not even art, because that's among the things gen AI is displacing the most effectively. Kinda, but also no. Yes, there's a lot of people (including me) who genuinely enjoy the output of these models; but art isn't only aesthetics, I observe it also being a peacock's tail, where the cost is the entire point. Why are originals more valuable than reproductions? Nobody who understands the tech can seriously claim that a robot with suitable brush and paints is incapable of perfectly reproducing any old masterwork down to the individual brush strokes — of course a robot can do that, the hardest part of that is compiling the list of requisite brush strokes, but that too can be automated. But such a copy, and lets say the paints were chemically perfect and also some blend of plant and petroleum derivatives so as to fool even a carbon-dating test, would never command as much money as the original unless someone deliberately mixed them up so that nobody would even know which was which. However, I don't know that this would ever help the masses. Perhaps a quadrillionaire in a space mansion would like to buy all of Earth and all the people on it, but that doesn't mean we'd get any better than being forced to LARP whatever folly* they chose for us. * https://en.wikipedia.org/wiki/Folly
You think that these ridiculously high wages that companies like Mercor are paying for data generation are "tanking" bargaining power? Its the complete opposite: there is now a massive sector of highly skilled, specialized labor that produces the very data which trains these models, a task that will not end as long as there is demand for newer and better and more specially trained models. That is a massive amount of bargaining power. It would take far more severe shocks to the system to kill the possibility of revolution, and that whatever that would be would be bad for everyone.
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Ridiculously high wages for jobs whose explicit purpose is to make human workers(including those partaking in those jobs) obsolete. THE reason why they pay so high is because their end-goal is not having to pay anyone anymore ever again. (or at least, only pay a comparatively tiny amount of people for producing the data)
My point of concern is that this domain may shrink to the empty set. There is no law of economics that says this will, with certainty, not happen. And while AI might be less power efficient than me on some tasks, power is cheap enough that I don't think the energy price floor affords my continued survival.
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> concern is that this domain may shrink to the empty set My argument is this is, as presented, baseless. > power is cheap enough that I don't think the energy price floor affords my continued survival Again, slaves were cheap enough. Industry is cheap enough. Yet global labour rates remain far from homogenous.
> All of them are enabled by big tech That's not true. All of the examples you mentioned are possible without Big Tech. There are F/LOSS and community supported alternatives for all of them. Big Tech might've contributed to parts of the technology that make these alternatives possible, but that could've been done by anyone else, and they are certainly not required to keep the technology functional today. Relying on Big Tech is a personal choice. None of these companies are essential to humanity. > none of them is based on surveillance, ads or social media. That's not true either. All Alphabet and Meta products are tied to and supported in some way by advertising. All of these companies were/are part of government surveillance programs. So you're highly overestimating the value of Big Tech, and highly underestimating the negative effects they've had, have, and will continue to have on humanity.
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>Big Tech might've contributed to parts of the technology that make these alternatives possible, but that could've been done by anyone else, and they are certainly not required to keep the technology functional today. Not only that, but big tech proprietary products have depended and depend heavily on F/LOSS and community supported code.
↙ time adjusted for second-chance
Lies, Damned Lies and Proofs: Formal Methods Are Not Slopless (lesswrong.com)
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The post says this in other words: in Lean, Rocq, or any other theorem prover, you get a formally-verified proof, but you do NOT get a formally verified theorem statement. So, even if the proof is correct, you need to determine if the theorem is what you want. Making that determination requires expertise. Since you cannot "run the theorem", you cannot vibe-code your way through it. E.g., there is no equivalent of "web app seems to be working!" You have to actually understand what the theorems are saying in a deep way.
A formal specification language is a programming language that we don't know how to compile. If we can use AI to automatically implement a formal spec, then that formal specification language has just become a programming language.
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It is the other way around: A programming language is a formal specification language that we know how to compile.
why do we invent these formal languages except to be more semantically precise than natural language? What does one gain besides familiarity by translation back into a more ambiguous language? Mis-defining concepts can be extremely subtle, if you look at the allsome quantifier https://dwheeler.com/essays/allsome.html you'll see that these problems predate AI, and I struggle to see how natural language is going to help in cases like the "All martians" case where the confusion may be over whether martians exist or not. Something relatively implicit.
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Because a spec in a formal language doesn't necessarily say what you really meant or intended. For instance, every programming language is also a formal language, and software never behaves the way its makers intended it to. Given all of that, natural language is more rigorous than any formal language. I don't understand how right after LLMs got started, a bunch of people on this website came out of the woodwork to tout that computer code was somehow more precise than natural language. What even is a software bug to these people, other than computer code being imprecise?
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We build pretty complex systems only based on "natural language" specifications. I think you are conflating specification ambiguity with verification accessibility. > What does one gain besides familiarity by translation back into a more ambiguous language? You gain intent verification. Formal languages are precise about implementation, but they are often opaque about intent. A formal specification can be "precisely wrong". E.g. you can write a perfectly precise Event-B spec that says "When the pedestrian button is pressed, the traffic light turns Green for cars"; the formalism is unambiguous, the logic is sound, the proof holds, but the intent is fatally flawed. Translating this back to natural language ("The system ensures that pressing the button turns the car light green") allows a human to instantly spot the error. > All Martians are green Modern LLMs are actually excellent at explicating these edge cases during back-translation if prompted correctly. If the formal spec allows vacuous truth, the back-translation agent can be instructed to explicitly flag existential assumptions. E.g. "For every Martian (assuming at least one exists), the color is Green", or "If there are no Martians, this rule is automatically satisfied". You are not translating back to casual speech; you are translating back to structured, explicit natural language that highlights exactly these kinds of edge cases.
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