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Auto rigging without manual weight painting or skeleton construction. Creators/game devs are going to love this. SkinTokens converts skinning to discrete token sequences. uses Qwen3-0.6B for precise skeleton and weight gen across any 3D asset.

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Wildminder

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17,331 Aufrufe • vor 8 Monaten •via X (Twitter)

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OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.
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OpenAI chairman Bret Taylor talks to about 100 CEOs every month. His answer to the cheap open-weight model panic: cheaper to train does not mean cheaper to use, and the number that decides it is token efficiency. "One thing that I think is a little bit overblown about these open weight models is they're not necessarily cheaper to run. Whether or not they're cheaper to train, you don't care. Because you're using just as many tokens. In fact, they may be less efficient." "There's this thing called token efficiency. And it turns out the frontier models are much, much more token efficient." "A token is to intelligence like a watt is to electricity... how many tokens does it take to complete a task? Not every token is actually equal." "For a lot of tasks, it turns out these frontier models from OpenAI and Anthropic are actually just better than these open weight models... just having open weights isn't actually the main thing driving any of those costs." Later in the same interview he goes after the billing unit itself: "It would be like if you signed up for Gmail and you paid for CPU cycle or something... where the world is going is paying for outcomes." The unresolved column: the chart CNBC airs mid-answer, from Artificial Analysis, prices a completed task at $0.94 on Kimi K3 against $2.75 on Claude Fable 5, efficiency folded in. If that gap holds, the premium he is defending gets earned on quality, not price. - Bret Taylor (Bret Taylor), OpenAI chairman and Sierra co-founder, on CNBC's Squawk Box.

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AI retargeting without manual bone mapping. This could be big for game animation. Different skeletons and body proportions have always made reusing animation a headache. You already have the mocap, but getting it onto a new character can mean another round of bone mapping and cleanup. UMR learns how points on body surfaces correspond, then uses that mapping to transfer motion without manually matching bones. The research focuses on humanoid robots. I see a lot of potential for game animation here. Reusing the same motion library across characters with very different proportions, with less setup for each rig, would make a big difference. I think we'll see people adapting this for 3D workflows pretty soon. For the method and experiments, here's the full paper -

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my ni-ki, i hope you know that you’ve done nothing wrong! you’re just an incredibly talented artist who puts his heart and soul into his work every single day to bring joy to millions. it’s completely unfair and wrong that people are trying to point fingers at you or direct their anger towards you right now. you’re not responsible for how others behave online, nor are you responsible for the actions of internet bullies who twist reality to spread hate. you cannot carry the weight of an entire online space, nor should you ever have to. it breaks my heart to know that you might see any of this toxicity or feel even a fraction of the weight of things that are completely out of your control. please remember that real engenes will always stand by your side. i love you so much, stay strong. 🤍
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my ni-ki, i hope you know that you’ve done nothing wrong! you’re just an incredibly talented artist who puts his heart and soul into his work every single day to bring joy to millions. it’s completely unfair and wrong that people are trying to point fingers at you or direct their anger towards you right now. you’re not responsible for how others behave online, nor are you responsible for the actions of internet bullies who twist reality to spread hate. you cannot carry the weight of an entire online space, nor should you ever have to. it breaks my heart to know that you might see any of this toxicity or feel even a fraction of the weight of things that are completely out of your control. please remember that real engenes will always stand by your side. i love you so much, stay strong. 🤍

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Choplifter (1982) Commodore 64 Originally released by Brøderbund in 1982 and created by Dan Gorlin for the Apple II, Choplifter was later ported to the Commodore 64 by Dane Bigham. This version uses multicolor bitmap mode for smooth parallax scrolling, cleverly extending the horizon into the screen border—no need for sprites for the wide chopper, tanks, or jets. In the game, you pilot a combat helicopter across a vast, side-scrolling desert landscape. Your mission is to blast tanks and dodge fast jets and homing drone missiles with precise forward-facing or rearward shots. Tap the fire button to shoot behind; hold it longer to pivot. Simultaneously, you must manage fuel and execute critical landing sequences. Destroy bunker doors under fire, touch down level to avoid crashing, and then wait as up to 16 frantic hostages sprint aboard. Watching some get gunned down or wander off adds heart-pounding tension. Multiple trips are required to rescue all 64 hostages for a perfect run. You have three lives before mission failure. Your 64 magazine rated it 60%, commenting, "Although not as good as Raid, this game is alright." What would you rate Choplifter?
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Choplifter (1982) Commodore 64 Originally released by Brøderbund in 1982 and created by Dan Gorlin for the Apple II, Choplifter was later ported to the Commodore 64 by Dane Bigham. This version uses multicolor bitmap mode for smooth parallax scrolling, cleverly extending the horizon into the screen border—no need for sprites for the wide chopper, tanks, or jets. In the game, you pilot a combat helicopter across a vast, side-scrolling desert landscape. Your mission is to blast tanks and dodge fast jets and homing drone missiles with precise forward-facing or rearward shots. Tap the fire button to shoot behind; hold it longer to pivot. Simultaneously, you must manage fuel and execute critical landing sequences. Destroy bunker doors under fire, touch down level to avoid crashing, and then wait as up to 16 frantic hostages sprint aboard. Watching some get gunned down or wander off adds heart-pounding tension. Multiple trips are required to rescue all 64 hostages for a perfect run. You have three lives before mission failure. Your 64 magazine rated it 60%, commenting, "Although not as good as Raid, this game is alright." What would you rate Choplifter?

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Seriously...GPT 6 can do this now? 😮 Here are the prompts i used: Try out Higgsfield: GPT-6 can now control After Effects directly, designing any motion graphic you want from a simple style reference and building it out layer by layer, all fully editable. Pair it with Higgsfield AI 🧩 to generate the UI graphics and assets you need, then let ChatGPT assemble everything for you. A game changer for motion designers, editors, and creators who want studio-quality graphics without the manual grind. Our mission is to create 100,000 new jobs for creatives, looking to learn and stay ahead in AI? Join GenHQ: #HiggsfieldAI #GPT6 #AfterEffects #MotionGraphics

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2.2+ ROAS ads are starting to look exactly like this not ugc creators, not influencers, and not doctors explaining things on camera just simple animated visuals that clearly show a problem and hint at the solution clean character design, strong curiosity hook, and a message anyone can understand instantly it feels easy to watch and doesn’t trigger that “this is an ad” reaction and that’s exactly why it converts so well no friction, no overthinking, just a visual that makes the problem obvious in seconds these animations are now being used across almost every niche weight loss, gut health, skincare, supplements, and more same visual style, different product behind it, and endless variations to test one concept can be turned into dozens of ads and scaled quickly rt + comment “animated” and I’ll send the setup (follow for dm)

David

63,514 Aufrufe • vor 6 Monaten

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Today, we’re sharing the first look at TollyLabs’ Uniswap v4 integration: infrastructure designed to bring every way to launch into one fully composable system on Arc. For too long, token launchpads have forced every project through the same rigid templates. Uniswap v4 hooks allow custom logic to operate throughout a market’s lifecycle, from initialization and swaps to liquidity changes and fee handling. For Tolly, that means launches can be designed around the asset, rather than forcing the asset to fit the launchpad. We’re building toward a spectrum of possibilities: • Plain tokens with a standard pad launch. • Creator-tax tokens with configurable buy and sell taxes, with proceeds directed to a wallet, burn, liquidity or another defined allocation. • Reflection tokens that automatically distribute USDC to holders pro rata, without manual claims or external keepers. • Programmable asset pairs that can trade against stablecoins, crypto assets and tokenized stocks, currencies and commodities. • Any combination of these mechanics. Most importantly, these are not isolated templates. They are composable building blocks. Our goal is a new kind of launchpad where creators can visually assemble sophisticated contracts and market structures without treating every launch as a custom engineering project. Simple when you need it. Fully programmable when you don’t. This is a preview of what we’ve been building. We’ll be explaining each component, and the system connecting them, over the coming days as we finish the work.

Tolly

67,804 Aufrufe • vor 21 Tagen

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!
2:50

learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

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