That's crazy when you think about it Qwen 3.8 27B is smarter than Opus 4.6 which was the absolute best model you could use 6-7 months ago. And now we have this Bonsai 2 retaining 98%+ of its intelligence... While weighing ONLY 5.9 GB 🤯 Intelligence density is absolutely crazy. Everyone has frontier intelligence at home now.

BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

NEW POLL: 57% of Connecticut voters say Ned Lamont does not deserve a third term. The race has narrowed to a single-digit margin and Ned Lamont‘s approval rating is under 50%. Our campaign has momentum, and when voters hear our message, our campaign is ahead in the polls. It’s time for change. Join us.

Meta’s Segment Anything Model (SAM) 3.1 is now available on Meta Model API, giving developers a fast and lightweight model for detection, segmentation and tracking in a single call on inference tuned for SAM 3.1's architecture. Use a short phrase to find objects in images and video. One API call returns detections, pixel-precise segmentation masks, and identity-preserving video tracks. Learn more and start building:

Full Jev Tutorial What it is, how you can build with it and what new applications it can unlock → 0:00 Intro → 0:34 Jev explained → 4:06 API setup → 5:59 Demo 1: Voice-controlled browser → 11:33 Demo 2: AI memory → 17:27 Demo 3: YouTube predictor

Am I the only one who sees Jev as amazing but terrible news for inference demand? Coding, and computer use are the huge uses of ai… now they need way (way way way) less tokens. (Obviously ppl will use more but…)

I've attempted to map everything in oncology in a public website and open source repo for all. The site is trying to get all cancers, products, technologies, bottlenecks, people, startup opportunities with 1000+ ideas for upgrading the field. If you have a loved one with cancer and you are technical or can engineer, go take a look, file improvement requests or bugs or help with the open repo and make this the best open and free info resource for individuals, researchers and educational use. This should save people time, aid AI oncology projects and generate positive action. If you are not technical just complain in this thread about broken or annoying or things you want and I'll fix them live. Some of the interesting pages: Treatments: A gallery of the molecules being used And targets: The technologies in oncology: 1100 ideas for helping oncology: Bottlenecks on oncology: Open questions (LETS GO RESEARCH PEOPLE) Startup requests (LETS GO STARTUP PEOPLE) Mechanics of cancer: Battlefronts: Isotope supply: Key papers: Pipeline funnels: Cancer by type: Institutional rankings: The startups: Heros and heroines : Key medical people: Here is the project roadmap: Models and data sets: There are other views as well, take a browse. Try making a PR if you have an upgrade to this on the repo here: If you are biologically/medically minded and something is wrong, file a bug as well or say on the thread and we will get it fixed live. If this is a useful project star the repo and help get it calibrated. I've tried to add some other languages but I cannot speak them so tell me if that doesnt work well. I believe we will crack oncology and having total information dominance is key to the problem. Let the feedback flow!

Connecticut starts early voting on October 19th. Yet Gov. Lamont has refused to accept all debates except for two—at the very end of the campaign after tens of thousand will have already voted. The WTNH debate isn’t until October 27th. The governor said no to Fox61 and Hartford Business Journal. He said no to a student-hosted debate. A Hartford Courant op-ed, co-authored by an informal Lamont advisor, called this race “a model of how American politics should work.” If so, then Lamont should say Yes to debates in every region BEFORE early voting begins. My attitude is this: Any time. Any place. Let’s debate before Connecticut votes.

This is what Insurance companies (and banks) should be TERRIFIED about. Muse found a new/cheaper policy, and canceled the old one. Inertia and information asymmetry will no longer be enough to keep customers. Exciting times!!

Extraordinary claims require extraordinary evidence so check out our release blog for more technical info: Join our waitlist for early access: Have technical chats and meme with us on Discord (rumors are good memers skip the line): We are so pumped this is in the hands of developers now, and we are just getting started!!!

Last month I wrote about how we can build a positive and safe future for everyone: Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens. The reality is: - People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned. There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind. - Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well. Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built. - Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators. - Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.

I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands. And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.