There is no inevitability in AI. We all have agency in what comes next:
Path 1: closed-source APIs, concentration of power, and a future decided by a handful of people in Silicon Valley and DC
Path 2: open-source AI, where everyone gets to participate, own, and build together, including orgs like the city of Rio.
Pick your path anon!
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.
The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.
Access to all other Claude models is not affected.
We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.
Read our full statement: https://t.co/bwn0sximKZ
One heuristic for making startups AI-proof is to make things human intelligence can't compete with either. For example, marketplaces are proof against AI competitors for the same reason they're proof against human ones.
Google's new algorithm just shrunk 31GB of memory down to 4GB π€―
TurboVec is a new open-source tool that stores the data your AI app searches through, using 16x less memory.
It runs on Google's TurboQuant, which skips the slow setup step every other tool needs.
β Faster search than the popular alternative (FAISS)
β Works on both Mac and standard servers
β Narrow results to exactly what you want
β Plugs straight into LangChain and LlamaIndex
Your data never leaves your machine. Runs fully offline, works with Python out of the box.
100% Open Source.
A 22-year-old graduate student in Kazakhstan got so angry at journal paywalls in 2011 that she built a pirate website holding 88 million scientific papers, and last month she turned the whole thing into an AI that lets you ask one question and get the actual research as the answer.
Her name is Alexandra Elbakyan, and the website is called Sci-Hub.
The AI she just launched is called Sci-Bot. It lives at https://t.co/6w0IBtOEYB and almost nobody outside academia knows it exists yet.
Here is the story, because it is one of the strangest things to happen in science publishing in the last 50 years.
Elbakyan was born in Almaty in 1988, the year the Soviet Union started to collapse. She taught herself programming at 12. She read Soviet science books that explained things her family used to call miracles. She got into computer security at university and graduated in 2009 with a degree she barely needed because by then she was already a serious hacker.
Alexandra moved to Moscow that fall. Then Germany. Then a research internship in the United States. She was working on brain-computer interfaces, the kind of research that requires you to read hundreds of papers a year just to keep up with the field.
And every single one of those papers was locked behind a journal paywall that cost between 30 and 50 dollars to read once.
She did the math. A graduate student in Kazakhstan could not afford to read science.
The first thing she did was learn how to get around the paywalls one paper at a time. She passed the trick around to other students. They asked her for papers constantly. She got tired of doing it manually.
So in September 2011, in three days, she wrote a script that automated the whole thing. A user pastes a DOI. The script logs in through a donated institutional credential. The paper comes back free. The website caches it.
The next person who asks for that paper gets it instantly because the previous request already saved a copy.
That was Sci-Hub. Three days of code. One graduate student. Done.
15 years later, the cache holds 88 million scientific papers. Almost every piece of scholarly literature published before 2020 is sitting on her servers. Researchers in 190 countries use it. Studies in Nature have shown that roughly half of all academic paper downloads worldwide now go through Sci-Hub, not the publishers who actually own the copyrights.
Elsevier sued her in 2015 and won a 15 million dollar judgment. She did not pay. The American Chemical Society sued her and won an injunction. She did not comply. Courts in India, France, Russia, and the UK have tried to block the domain. She just moves it. https://t.co/3sAWJzNe8I. https://t.co/tGIETesZ8i. https://t.co/H5WQ1f9lqR. The site has had over 20 domains and is still up.
Nature put her on its list of the 10 people who mattered most to science in 2016. The New York Times compared her to Edward Snowden. The Verge called her the pirate queen of science.
She has not been to the United States in over a decade because she would be arrested at the airport.
The Sci-Bot launch in April 2026 is the part that nobody is talking about.
She took the 88 million paper database and put a small language model on top of it. You ask a question in plain English. The model searches the entire shadow library, pulls the relevant papers, synthesizes an answer grounded in real citations, and links you to the full text of every source. Free. No login. No institutional credential. No paywall.
Three real scientists tested it for a Chemical and Engineering News article last month. They asked it medical and chemistry questions. The radiologist said the answer he got was usable. The chemist said the gaps in recent literature were obvious but the older science was solid. The publisher community is furious.
What she built is what the paid academic AI tools are trying to build. Except the paid ones are limited to what their parent publisher legally owns. Hers is limited to almost nothing.
Alexandra still lives somewhere in Russia. She does not give her address. She does not do video interviews. She gives talks over Skype with the camera off. She runs the largest illegal library in human history from a laptop and a donation page.
A graduate student who could not afford to read science built the system the entire scientific community now quietly depends on.
The publishers have spent a decade trying to shut her down.
She just shipped an AI that makes their entire business model outdated.
Qwen 3.7-max beats Opus 4.7 and GPT-5.5
We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head.
Qwen 3.7-Max: training cost $1.32, bot improvement +56%
Claude Opus 4.7: training cost $12.15, bot improvement +28%
GPT-5.5: training cost $2.85, bot improvement +7%
Qwen won on every dimension - biggest jump, 9Γ cheaper than Claude, 2Γ cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.
Many villages across the UK have repurposed iconic red telephone boxes into tiny community libraries, where you can take a book and leave one for someone else to enjoy too if you want to