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Interesting things

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1,380,305 просмотров • 1 год назад •via X (Twitter)

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I have found it really useful to convert notes/essays/paper into Artifacts. And it seems Fable 5 is really good at it. Artifacts are great for building deeper intuition on any topic. Just shared this one I did on Satya's latest essay on the Reverse Information Paradox.
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I have found it really useful to convert notes/essays/paper into Artifacts. And it seems Fable 5 is really good at it. Artifacts are great for building deeper intuition on any topic. Just shared this one I did on Satya's latest essay on the Reverse Information Paradox.

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There are a LOT of amazing "pose reference packs" for artists. I love them. Wide variety of people and a wide variety of costumes etc. Incredibly useful for drawing from. Also, highly recommended to draw from still frames of whatever actiony videos you can find. Poses are great - but there's information in movement too. Here's one that's fun - by Elsaucepapii on Facebook. Set it to half speed. Scroll it. Stop on a frame. Draw.

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The #CoreIgnitionDrop Season 1 is ending today, September 11th at 18:29 UTC and Season 2 is starting tomorrow, September 12th at 00:00 UTC🔥 Here is some useful information: 🔸Season 1 rewards will remain claimable (for 6 months as previously announced); 🔸Season 1 badges and invitees will be carried over to Season 2; 🔸All Season 1 participants who don't have an invite code will have one once logged in for Season 2; 🔸Sparks will be reset to 0 when Season 2 starts; 🔸The 3rd claiming phase for Season 1 will take place after Season 2 starts. Stay tuned and get ignited! 🔗

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Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

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A Nairobi man is seeking help in tracing two women suspected of drugging and robbing him of valuables worth over Ksh 700,000 after he invited the suspects to his residence following a night out last weekend in Ongata Rongai, Kajiado County. According to available information and CCTV footage, one of the women was seen stepping outside to conduct what appears to be surveillance, while her accomplice allegedly packed the stolen items inside the house. The suspects made away with a silver Apple MacBook Pro 16-inch, an iPhone 14 Pro Max, an iPod Pro Max, a pair of Apple AirPods Max headphones, and KSh 52,000 which was transferred from the victim’s M-PESA account to a phone number that has since been switched off. The footage shows the two women leaving the premises at around 2:55 PM on Saturday while carrying bags believed to contain the stolen items. The victim was reportedly unconscious inside the apartment at the time of the theft. He is now appealing to the public for assistance in identifying the two suspects, hoping that the CCTV footage will prompt anyone with useful information to come forward.

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Trump is right to call Kristen Welker out. If she will lie about his endorsement record, she will lie about anything. Bill Maher already corrected himself on air after Trump sent him the numbers. Maher is a comedian. Welker is supposed to be a news anchor. Calling a mid-90s win rate “mixed results” is not a slip. It is a narrative. The press has done this for decades. Soften every win. Inflate every loss. Then act shocked when people stop trusting the product. Calling that out is one of the most useful things Trump does. Accurate information is not optional. A country that lets anchors rewrite the scoreboard on purpose is not informing the public. It is managing it. Report it to the FCC. Fine the network. Make the lie expensive.

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My Grok Bot + Kimi K3 Made $2,200 While I Was Away I gave them the job, closed the laptop, and came back to this agents were doing the research, checking the numbers, passing work to each other, and Kimi K3 was deciding what needed to happen next I wasn’t sitting there moving results from one chat to another, I wasn’t writing the next prompt, and I wasn’t the guy keeping everything moving RESEARCH was finding the information ANALYSIS was turning it into something useful VERIFY was catching bad numbers and old data KIMI K3 was keeping the whole thing together The crazy part is that I wasn’t even there I set the goal, the agents took the work, and the system kept going while I was away $2,200 later, I came back to the result Maybe the biggest upgrade with AI isn’t making it smarter Maybe it’s finally getting it to stop waiting for us to tell it what to do next

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LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

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AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference
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AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

12,064 просмотров • 26 дней назад