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Training Wan 2.1-1.3B to use Qwen3-VL-2B text encoder. Doing 33% text only, 33% VL only, and 33% both. Just pretraining the two text input linear layers for now. It is amazing how quickly a model can adapt to a different text encoder. This is 25,750 steps, BS of 10.

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Ostris

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19,095 görüntüleme • 1 gün önce •via X (Twitter)

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A quick way to improve how text behaves in your app is to use `text-wrap: balance`. It distributes text evenly across each line, avoiding orphaned words. It is only supported for a limited number lines (<6-10 depending on the browser), which means it's best used for headings or short paragraphs.
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A quick way to improve how text behaves in your app is to use `text-wrap: balance`. It distributes text evenly across each line, avoiding orphaned words. It is only supported for a limited number lines (<6-10 depending on the browser), which means it's best used for headings or short paragraphs.

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Just doing a first test with #runwayml new video to video. No image input yet, only text atm. But driving the animation is a good first step.
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Just doing a first test with #runwayml new video to video. No image input yet, only text atm. But driving the animation is a good first step.

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react-native-enriched 🤝 &lt;EnrichedText&gt; We just dropped a dedicated text component, making it a complete rich-text solution. Now it only takes a few lines of code to get your inputs and display text working seamlessly together using the exact same format.
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react-native-enriched 🤝 &lt;EnrichedText&gt; We just dropped a dedicated text component, making it a complete rich-text solution. Now it only takes a few lines of code to get your inputs and display text working seamlessly together using the exact same format.

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[to the tune of happy birthday] text turns into speech text turns into speech the model is launching text turns into speech
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[to the tune of happy birthday] text turns into speech text turns into speech the model is launching text turns into speech

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hey babe &lt;33 wanna see more? text me
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hey babe &lt;33 wanna see more? text me

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Bismillah. Image-To-Text Converter for Non-English PDFs is now available! It converts books in Arabic, Urdu, Persian, Spanish, etc. with amazing accuracy Currently this just a MVP version that extracts text but there is a lot on the roadmap iA Link below to test it out!
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Bismillah. Image-To-Text Converter for Non-English PDFs is now available! It converts books in Arabic, Urdu, Persian, Spanish, etc. with amazing accuracy Currently this just a MVP version that extracts text but there is a lot on the roadmap iA Link below to test it out!

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This is kind of insane but making it official. You can literally text TEXTAI (839824) and AI answers your questions. Right in your texts. No bs. No app. No log in. Just text it.
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This is kind of insane but making it official. You can literally text TEXTAI (839824) and AI answers your questions. Right in your texts. No bs. No app. No log in. Just text it.

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STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.
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STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.

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🆕 Now you can use Gemini in Google Sheets right from a cell to quickly generate, summarize, and categorize text. →
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🆕 Now you can use Gemini in Google Sheets right from a cell to quickly generate, summarize, and categorize text. →

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[Switch 2] GameChat supports text-to-speech. If enabled, you can type in text and the system will read it for you. While not explicitly confirmed, USB keyboards should be supported too (the Switch 1 already supports them for text input).
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Rive text is live!! Animatable, variable, dynamic text for runtime, with full support for any language. There are a MONSTER amount of new things to play around with! 🚀 Create Text objects, styles, runs, and modifiers. 🚀 Support for any language. LTR, RTL, special characters/features, ligatures, etc. 🚀 Animate variable fonts. 🚀 Google Fonts are provided by default, but you can upload custom fonts. 🚀 Runs are a way to segment a text field into different sections. You can target runs at runtime to change their content dynamically. See documentation here: 🚀 Apply different text styles to runs. 🚀 Convert text to paths (flatten). 🚀 Text modifiers are a powerful way to add layered animation to text, while maintaining its editability! 🚀 Use transform constraints to resize graphics to text objects. Coming soon: 🚧 Ability to export fonts out of band or via CDN. They are currently packaged in the .riv file.
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Rive text is live!! Animatable, variable, dynamic text for runtime, with full support for any language. There are a MONSTER amount of new things to play around with! 🚀 Create Text objects, styles, runs, and modifiers. 🚀 Support for any language. LTR, RTL, special characters/features, ligatures, etc. 🚀 Animate variable fonts. 🚀 Google Fonts are provided by default, but you can upload custom fonts. 🚀 Runs are a way to segment a text field into different sections. You can target runs at runtime to change their content dynamically. See documentation here: 🚀 Apply different text styles to runs. 🚀 Convert text to paths (flatten). 🚀 Text modifiers are a powerful way to add layered animation to text, while maintaining its editability! 🚀 Use transform constraints to resize graphics to text objects. Coming soon: 🚧 Ability to export fonts out of band or via CDN. They are currently packaged in the .riv file.

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Text-to-image diffusion transformer models learn to align text and image representations as a byproduct of their conditional denoising task. By taking the dot product between the text and image representations of a DiT model (like Flux 2), you can create rich saliency maps.
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Text-to-image diffusion transformer models learn to align text and image representations as a byproduct of their conditional denoising task. By taking the dot product between the text and image representations of a DiT model (like Flux 2), you can create rich saliency maps.

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Get beautiful text animations. We built a hand-crafted collection of text animations and wrapped them as a SKILL for your AI agent. Use it with Remotion, Motion, GSAP, WAAPI, or any animation library. It comes with clear animation specs - pacing, curves, transitions - so your output looks great from the first prompt. Install it with: npx skills add pixel-point/animate-text --skill animate-text Use it /animate-text apply soft-blur-in to hero section text
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Get beautiful text animations. We built a hand-crafted collection of text animations and wrapped them as a SKILL for your AI agent. Use it with Remotion, Motion, GSAP, WAAPI, or any animation library. It comes with clear animation specs - pacing, curves, transitions - so your output looks great from the first prompt. Install it with: npx skills add pixel-point/animate-text --skill animate-text Use it /animate-text apply soft-blur-in to hero section text

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this is a wild one! pusa, a new video model that pretty much supports every image/text/video-video generation task just got open-sourced the kicker? training cost only $100 🤯
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this is a wild one! pusa, a new video model that pretty much supports every image/text/video-video generation task just got open-sourced the kicker? training cost only $100 🤯

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I guess I finally found a use case for nested buttons. It was the only way to get text truncation to work here.
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I guess I finally found a use case for nested buttons. It was the only way to get text truncation to work here.

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Looking for legit meetup sessions? Text me your zip code to book a session Telegram Text me for FaceTime shows too No BS!!!!!
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Looking for legit meetup sessions? Text me your zip code to book a session Telegram Text me for FaceTime shows too No BS!!!!!

Thereal DC🍃😈

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The `rebel-large` model is awesome for relation extraction 🔗 Paired with CUDA, it’s blazing fast ⚡️. With LlamaIndex 🦙 🦙, we can now build a knowledge graph over any text data super quickly! 🕸️ Full Colab notebook showing how you can use it:
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The `rebel-large` model is awesome for relation extraction 🔗 Paired with CUDA, it’s blazing fast ⚡️. With LlamaIndex 🦙 🦙, we can now build a knowledge graph over any text data super quickly! 🕸️ Full Colab notebook showing how you can use it:

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Gemini 3.1 Flash TTS You can steer how the model speaks by dropping tags right into the text: "[like dracula] Hey there, I'm a new text to speech model and I can say things in many different ways. How can I help you today?" 70+ languages, style prompting via natural language
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Gemini 3.1 Flash TTS You can steer how the model speaks by dropping tags right into the text: "[like dracula] Hey there, I'm a new text to speech model and I can say things in many different ways. How can I help you today?" 70+ languages, style prompting via natural language

Replicate

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Get sculpting-level details in seconds. Meshy 6 Preview is out now for both Text-to-3D and Image-to-3D, delivering a significant leap in mesh quality. This is just the start.
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Get sculpting-level details in seconds. Meshy 6 Preview is out now for both Text-to-3D and Image-to-3D, delivering a significant leap in mesh quality. This is just the start.

MeshyAI

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