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Incomplete documentation leads to costly rework. Virtual Walkthrough captures high-res photos during scanning & integrates them directly into your 3D model. Read labels. Spot details. Verify remotely. Available now on Biz & Enterprise. Book a demo:

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20,219 görüntüleme • 8 ay önce •via X (Twitter)

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Introducing powered by Over the Reality. FreeGaussian is a new way to transform your existing photos, videos, and 360 captures for free into high-quality 3D Gaussian splats, directly in your browser. Whether you already have: ↳ a 360 recording captured with any 360 camera ↳ a set of photos ↳ or even a simple video from a place you visited you can now turn it into a photorealistic 3D Gaussian splat. Just go to upload your content, and let the reconstruction happen. Once it’s ready, you can download your 3D Gaussian splat and point cloud, and use them across your own projects, pipelines, or applications.
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Introducing powered by Over the Reality. FreeGaussian is a new way to transform your existing photos, videos, and 360 captures for free into high-quality 3D Gaussian splats, directly in your browser. Whether you already have: ↳ a 360 recording captured with any 360 camera ↳ a set of photos ↳ or even a simple video from a place you visited you can now turn it into a photorealistic 3D Gaussian splat. Just go to upload your content, and let the reconstruction happen. Once it’s ready, you can download your 3D Gaussian splat and point cloud, and use them across your own projects, pipelines, or applications.

Over the Reality 🌐

86,201 görüntüleme • 2 ay önce

Ever tried 3D scanning a printed circuit board (PCB)? Capturing tiny solder joints, dense routing, and reflective surfaces is no easy task. In this demo, the AutoScan Inspec 2 takes on a highly complex electronic component to show what high-accuracy 3D metrology can achieve. Watch how it captures fine micro-details and converts them into a precise 3D model ready for reverse engineering, inspection, and quality control workflows. ---- From automotive body panels to aerospace components, precision inspection is becoming a critical part of modern manufacturing. This new whitepaper explores how high-accuracy 3D metrology is helping engineers capture full-field data, reduce inspection bottlenecks, and improve quality assurance workflows with micron-level precision. Get the whitepaper here: #3dscanning #engineering #technology Shining 3D, Inc.
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Ever tried 3D scanning a printed circuit board (PCB)? Capturing tiny solder joints, dense routing, and reflective surfaces is no easy task. In this demo, the AutoScan Inspec 2 takes on a highly complex electronic component to show what high-accuracy 3D metrology can achieve. Watch how it captures fine micro-details and converts them into a precise 3D model ready for reverse engineering, inspection, and quality control workflows. ---- From automotive body panels to aerospace components, precision inspection is becoming a critical part of modern manufacturing. This new whitepaper explores how high-accuracy 3D metrology is helping engineers capture full-field data, reduce inspection bottlenecks, and improve quality assurance workflows with micron-level precision. Get the whitepaper here: #3dscanning #engineering #technology Shining 3D, Inc.

Wevolver

36,150 görüntüleme • 2 ay önce

We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:
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We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the cost 🍌⚡️ 1️⃣ Introducing Nano Banana 2 Lite: Our fastest and most cost-efficient Gemini Image model yet delivers text-to-image outputs in under 4 seconds. Now available via the Gemini API and Google AI Studio, and rolling out soon across @NotebookLM, Google Flow, Google Gemini, Stitch by Google, Google Search and Google Photos. 2️⃣ Gemini Omni Flash in Public Preview: Our natively multimodal model for cost-efficient video generation and conversational editing. Now available via the Gemini API, Google AI Studio, and Gemini Enterprise Agent Platform so you can integrate the model into your workflow. While exciting on their own, the real magic happens when you build using these models together. Watch how our interior design demo integrates Nano Banana 2 Lite and Omni to instantly reimagine any space. Upload a photo, swipe through tailored design concepts, and see Omni bring the details to life in cinematic motion. Try out the demo app in AI Studio:

Google AI

119,113 görüntüleme • 18 gün önce

The AR GenAI solution available on AR Code SaaS continues to demonstrate how fast immersive AR content can now be created from a single image. In this new demo, a contemporary art statuette is captured with a single photo, then instantly transformed into a fully optimized 3D model ready for augmented reality display. As shown in the video, the generated 3D artwork is automatically published through an AR QR Code, allowing anyone to launch the experience instantly in augmented reality directly from their smartphone. No app required. This example highlights a powerful capability of AR GenAI by AR Code: artworks can now be easily digitized and transformed into interactive AR experiences starting from just one photo. For contemporary art galleries, museums, cultural institutions, artists, and art marketplaces, this opens a new path to quickly create scalable 3D collections and immersive exhibitions. Object Photo → AI 3D generation → AR QR Code → Instant WebAR What previously required complex 3D scanning setups, photogrammetry pipelines, or specialized expertise can now be achieved in minutes. AR GenAI makes it possible to digitize artworks, publish AR experiences, and scale immersive cultural content faster than ever. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #ImageTo3D #ImageToAR #WebAR #AugmentedReality #DigitalArt #Museums #ArtTech
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The AR GenAI solution available on AR Code SaaS continues to demonstrate how fast immersive AR content can now be created from a single image. In this new demo, a contemporary art statuette is captured with a single photo, then instantly transformed into a fully optimized 3D model ready for augmented reality display. As shown in the video, the generated 3D artwork is automatically published through an AR QR Code, allowing anyone to launch the experience instantly in augmented reality directly from their smartphone. No app required. This example highlights a powerful capability of AR GenAI by AR Code: artworks can now be easily digitized and transformed into interactive AR experiences starting from just one photo. For contemporary art galleries, museums, cultural institutions, artists, and art marketplaces, this opens a new path to quickly create scalable 3D collections and immersive exhibitions. Object Photo → AI 3D generation → AR QR Code → Instant WebAR What previously required complex 3D scanning setups, photogrammetry pipelines, or specialized expertise can now be achieved in minutes. AR GenAI makes it possible to digitize artworks, publish AR experiences, and scale immersive cultural content faster than ever. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #ImageTo3D #ImageToAR #WebAR #AugmentedReality #DigitalArt #Museums #ArtTech

Maxime Maisonneuve

19,003 görüntüleme • 4 ay önce

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!
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Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

95,991 görüntüleme • 7 ay önce

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this
2:06

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 görüntüleme • 1 ay önce

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:
19:21

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

Santiago

255,433 görüntüleme • 1 yıl önce

Last year, during tax season. I had 50+ receipts. Some in my email. Some on WhatsApp. Some in my gallery. And a few, I couldn’t even remember where I saved them. What should’ve taken a few hours turned into late nights, frustration, and second-guessing everything. Because the real problem isn’t filing taxes. It’s this: → Collecting documents → Organizing them → Verifying if everything is correct That’s where most people struggle. This year, I tried something different. Instead of chasing files everywhere, I built a simple, clean system using Wondershare PDFelement. And honestly, it changed everything. Here’s how my workflow looked. I started by scanning all my paper receipts directly from my phone using the Receipt Assistant. No manual typing. No guesswork. It automatically extracted details like: • Merchant • Date • Amount • Taxes Everything turned into searchable PDFs instantly. Then came the best part. All files were automatically saved to the cloud. So, I could: → Scan on mobile → Manage on desktop → Access everything, anytime No more “where did I save that?” moments. But what impressed me most. Every extracted detail is traceable. I could click any number and instantly jump back to the exact spot in the original receipt. No more cross-checking line by line. And when it was time to organize everything. • Exported all data into Excel → full expense overview • Merged multiple files into one clean PDF • Edited tax documents directly (no extra tools needed) Before final submission, I used: • AI Assistant → to summarize & cross-check documents • Smart Redact → to hide sensitive information Everything felt controlled, clean, and secure. That’s when it hit me: A good tax system isn’t about working harder. It’s about having a clear, traceable workflow that removes chaos. Suppose your files are still scattered across folders, emails, and screenshots. That is exactly why tax season feels exhausting. Search Wondershare PDFelement and try it free → #wondersharepdfelement #pdfelement #FileWithPDFelement #TaxSeason
3:40

Last year, during tax season. I had 50+ receipts. Some in my email. Some on WhatsApp. Some in my gallery. And a few, I couldn’t even remember where I saved them. What should’ve taken a few hours turned into late nights, frustration, and second-guessing everything. Because the real problem isn’t filing taxes. It’s this: → Collecting documents → Organizing them → Verifying if everything is correct That’s where most people struggle. This year, I tried something different. Instead of chasing files everywhere, I built a simple, clean system using Wondershare PDFelement. And honestly, it changed everything. Here’s how my workflow looked. I started by scanning all my paper receipts directly from my phone using the Receipt Assistant. No manual typing. No guesswork. It automatically extracted details like: • Merchant • Date • Amount • Taxes Everything turned into searchable PDFs instantly. Then came the best part. All files were automatically saved to the cloud. So, I could: → Scan on mobile → Manage on desktop → Access everything, anytime No more “where did I save that?” moments. But what impressed me most. Every extracted detail is traceable. I could click any number and instantly jump back to the exact spot in the original receipt. No more cross-checking line by line. And when it was time to organize everything. • Exported all data into Excel → full expense overview • Merged multiple files into one clean PDF • Edited tax documents directly (no extra tools needed) Before final submission, I used: • AI Assistant → to summarize & cross-check documents • Smart Redact → to hide sensitive information Everything felt controlled, clean, and secure. That’s when it hit me: A good tax system isn’t about working harder. It’s about having a clear, traceable workflow that removes chaos. Suppose your files are still scattered across folders, emails, and screenshots. That is exactly why tax season feels exhausting. Search Wondershare PDFelement and try it free → #wondersharepdfelement #pdfelement #FileWithPDFelement #TaxSeason

Vikas Singh

11,795 görüntüleme • 3 ay önce

Introducing Neural Capture Version 2 on CorOS 3.3.0 and NanOS 2.2.0 - available now! Neural Capture Version 2 is a new cloud-trained version of Neural Capture that delivers higher resolution, greater realism, and improved dynamic response. By shifting the training process to Cortex Cloud, Capture V2 uses a more advanced algorithm that can model complex analog behavior beyond what is possible with on-device processing. Capture V2 brings major improvements to devices that rely heavily on touch and dynamics. These devices are notoriously difficult to capture accurately, making V2 the most authentic solution on the market for reproducing the dynamic cleanup behavior of a vintage fuzz, the natural bloom of a sagging power amp, and the fast transient response of a studio compressor. V2 Capture creation is currently available only on Quad Cortex. NanOS 2.2.0 introduces the Capture 2 Player, which lets Nano Cortex load and play V2 Captures created on Quad Cortex. Support for creating V2 Captures directly on Nano Cortex is under development. CorOS 3.3.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices 🔥 29 new virtual devices: ⚙️⚙️ Dumbbell ODS (Dumble® Overdrive Special®) ⚙️⚙️ 17 Cabs ⚙️⚙️ Mono Synth ⚙️⚙️ Micro Processor (ST) (Eventide® Micropitch Delay®) ⚙️⚙️ Pattern Tremolo ⚙️⚙️ Bit-Crusher Engine (M) ⚙️⚙️ Bit-Crusher (ST) ⚙️⚙️ Phase-Locked Loop (EarthQuaker Devices® Data Corrupter®) ⚙️⚙️ 81 Creations Drive (1981 Inventions® DRV®) ⚙️⚙️ Aggi Sub Octaver (Aguilar® Octamizer®) ⚙️⚙️ Spring Reverb Engine (M) ⚙️⚙️ Spring Reverb Engine (ST) ⚙️⚙️ Auto Wah 🔥 Several quality of life improvements Cortex Control 1.4.0 Cortex Control has been updated to support CorOS 3.3.0. To create a Neural Capture V2, you need the latest versions of CorOS and Cortex Control. NanOS 2.2.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices (downloadable from the official Neural DSP Cortex Cloud profile) 🔥 Cortex Cloud offline mode 🔥 Automatic Sum to Mono 🔥 Tremolo 🔥 Capture auditioning 🔥 Cloud backups Read everything about this huge update here: 🗞️
1:22

Introducing Neural Capture Version 2 on CorOS 3.3.0 and NanOS 2.2.0 - available now! Neural Capture Version 2 is a new cloud-trained version of Neural Capture that delivers higher resolution, greater realism, and improved dynamic response. By shifting the training process to Cortex Cloud, Capture V2 uses a more advanced algorithm that can model complex analog behavior beyond what is possible with on-device processing. Capture V2 brings major improvements to devices that rely heavily on touch and dynamics. These devices are notoriously difficult to capture accurately, making V2 the most authentic solution on the market for reproducing the dynamic cleanup behavior of a vintage fuzz, the natural bloom of a sagging power amp, and the fast transient response of a studio compressor. V2 Capture creation is currently available only on Quad Cortex. NanOS 2.2.0 introduces the Capture 2 Player, which lets Nano Cortex load and play V2 Captures created on Quad Cortex. Support for creating V2 Captures directly on Nano Cortex is under development. CorOS 3.3.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices 🔥 29 new virtual devices: ⚙️⚙️ Dumbbell ODS (Dumble® Overdrive Special®) ⚙️⚙️ 17 Cabs ⚙️⚙️ Mono Synth ⚙️⚙️ Micro Processor (ST) (Eventide® Micropitch Delay®) ⚙️⚙️ Pattern Tremolo ⚙️⚙️ Bit-Crusher Engine (M) ⚙️⚙️ Bit-Crusher (ST) ⚙️⚙️ Phase-Locked Loop (EarthQuaker Devices® Data Corrupter®) ⚙️⚙️ 81 Creations Drive (1981 Inventions® DRV®) ⚙️⚙️ Aggi Sub Octaver (Aguilar® Octamizer®) ⚙️⚙️ Spring Reverb Engine (M) ⚙️⚙️ Spring Reverb Engine (ST) ⚙️⚙️ Auto Wah 🔥 Several quality of life improvements Cortex Control 1.4.0 Cortex Control has been updated to support CorOS 3.3.0. To create a Neural Capture V2, you need the latest versions of CorOS and Cortex Control. NanOS 2.2.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices (downloadable from the official Neural DSP Cortex Cloud profile) 🔥 Cortex Cloud offline mode 🔥 Automatic Sum to Mono 🔥 Tremolo 🔥 Capture auditioning 🔥 Cloud backups Read everything about this huge update here: 🗞️

Neural DSP

36,094 görüntüleme • 7 ay önce

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:
3:36

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:

Andrew Ng

253,812 görüntüleme • 1 yıl önce

A love letter to one of the coolest guitars of all time. Is there a more iconic guitar player on Earth than Keith Richards? For more than six decades, the “Human Riff” has been the heartbeat of The Rolling Stones, inspiring millions of fans and musicians the world over to get out of their seats and rock ’n’ roll. It’s difficult to imagine the popular music landscape without the monolithic presence of Keith Richards looming over it with impossible cool, godlike nonchalance, and, of course, impeccable taste in guitars. Ask any guitarist which instrument of Keith’s they desire the most, and we’re willing to bet his black 1960 Gibson ES-355 is top of the list. Keith first used an ES-355 back in 1969, taking it out on the road and into the studio during the legendary recording sessions for Sticky Fingers and Exile on Main St. His black 1960 model has also been a staple of each and every planet-straddling Rolling Stones tour since 1997. Now, we are proud to present the Keith Richards 1960 ES-355 Collector’s Edition guitar, an exacting replica of the Gibson ES-355 he made famous. Handcrafted in the Gibson Custom Shop in Nashville, Tennessee, it’s not just a tribute to Keith’s original guitar; it’s effectively a clone, employing new 3D scanning technology, identical materials and construction methods, and meticulous Murphy Lab aging. The devil is in the details, and this guitar captures every nuance of the original, right down to the sonics. Limited to only 150 guitars worldwide;. 100 hand-signed by Keith Richards on the F-hole label, and 50 hand-signed on both the F-hole label and the back of the headstock. Read more about our partnership here: #Gibson #GibsonCustom #KeithRichards #ES355
0:30

A love letter to one of the coolest guitars of all time. Is there a more iconic guitar player on Earth than Keith Richards? For more than six decades, the “Human Riff” has been the heartbeat of The Rolling Stones, inspiring millions of fans and musicians the world over to get out of their seats and rock ’n’ roll. It’s difficult to imagine the popular music landscape without the monolithic presence of Keith Richards looming over it with impossible cool, godlike nonchalance, and, of course, impeccable taste in guitars. Ask any guitarist which instrument of Keith’s they desire the most, and we’re willing to bet his black 1960 Gibson ES-355 is top of the list. Keith first used an ES-355 back in 1969, taking it out on the road and into the studio during the legendary recording sessions for Sticky Fingers and Exile on Main St. His black 1960 model has also been a staple of each and every planet-straddling Rolling Stones tour since 1997. Now, we are proud to present the Keith Richards 1960 ES-355 Collector’s Edition guitar, an exacting replica of the Gibson ES-355 he made famous. Handcrafted in the Gibson Custom Shop in Nashville, Tennessee, it’s not just a tribute to Keith’s original guitar; it’s effectively a clone, employing new 3D scanning technology, identical materials and construction methods, and meticulous Murphy Lab aging. The devil is in the details, and this guitar captures every nuance of the original, right down to the sonics. Limited to only 150 guitars worldwide;. 100 hand-signed by Keith Richards on the F-hole label, and 50 hand-signed on both the F-hole label and the back of the headstock. Read more about our partnership here: #Gibson #GibsonCustom #KeithRichards #ES355

Gibson

107,736 görüntüleme • 6 ay önce

Meet My AI Ears. A lot of folks ask me how I capture ASMR video and audio for training of AI? I always use Binaural 3D audio and have for decades in different forms. But how? A Brief History of Binaural Recording Binaural recording, the foundation of 3D audio, dates back to 1881 when French inventor Clément Ader created the first system using multiple telephone transmitters at the Paris Opera to transmit stereo sound to listeners, simulating spatial presence. By the 1920s, patents like W. Bartlett Jones’ 1927 filing advanced devices for capturing and reproducing “binaural” signals. The 1930s saw Alan Blumlein’s work on stereophonic sound, which he termed “binaural,” laying groundwork for modern stereo. Commercial milestones hit in the 1950s with binaural records from labels like Cook Laboratories and the first binaural reel-to-reel tapes. A resurgence came in the 1970s with Neumann’s KU-80 dummy head, the first commercial binaural system. Today, it’s integral to VR, ASMR, and immersive media. The Technology of Binaural 3D Audio At its core, binaural recording mimics human hearing by using two microphones placed in ear-shaped molds or a dummy head, separated like human ears (typically 14-18 cm apart). This captures spatial cues: interaural time differences (ITD) for sound arrival timing, interaural level differences (ILD) for volume variations, and head-related transfer functions (HRTF) that account for how the head, torso, and pinnae filter sounds. The result? A 3D soundscape that tricks the brain into perceiving direction, distance, and elevation when played back via headphones—no speakers needed for immersion. Advanced setups use omnidirectional capsules (e.g., DPA 4060) for high-fidelity capture, often in silicone ears to replicate natural diffraction. I use the 3DIO Microphones today but I would cover a dummy head in texture material and place two stereo (4 channels) microphones in each ear. I would then mix down the resulting signals into stereo. The 3DIO series features dual omnidirectional capsules in realistic silicone ear molds, spaced 14 cm apart for compact, accurate 3D capture based on over 13 years of research into human hearing. Models like the Free Space Pro II use premium DPA 4060 CORE capsules for ultra-low noise and high sensitivity, delivering stereo output ideal for immersive applications like game audio. Today just about any ASMR producer uses these. But I use them to capture, curate and archive sound and video we will lose or just about lost for AI training in a way no model or AI company is doing today. I am duplicating the human 3D binaural audio experience and memory. Below is a crude demonstration. If you can listen in headphones. Or turn your phone sideways to feel the audio space. I’ll have a far more professional demo soon to show the real power of 3D audio. (Oh that music is a MIDI player that uses disks to play).
0:35

Meet My AI Ears. A lot of folks ask me how I capture ASMR video and audio for training of AI? I always use Binaural 3D audio and have for decades in different forms. But how? A Brief History of Binaural Recording Binaural recording, the foundation of 3D audio, dates back to 1881 when French inventor Clément Ader created the first system using multiple telephone transmitters at the Paris Opera to transmit stereo sound to listeners, simulating spatial presence. By the 1920s, patents like W. Bartlett Jones’ 1927 filing advanced devices for capturing and reproducing “binaural” signals. The 1930s saw Alan Blumlein’s work on stereophonic sound, which he termed “binaural,” laying groundwork for modern stereo. Commercial milestones hit in the 1950s with binaural records from labels like Cook Laboratories and the first binaural reel-to-reel tapes. A resurgence came in the 1970s with Neumann’s KU-80 dummy head, the first commercial binaural system. Today, it’s integral to VR, ASMR, and immersive media. The Technology of Binaural 3D Audio At its core, binaural recording mimics human hearing by using two microphones placed in ear-shaped molds or a dummy head, separated like human ears (typically 14-18 cm apart). This captures spatial cues: interaural time differences (ITD) for sound arrival timing, interaural level differences (ILD) for volume variations, and head-related transfer functions (HRTF) that account for how the head, torso, and pinnae filter sounds. The result? A 3D soundscape that tricks the brain into perceiving direction, distance, and elevation when played back via headphones—no speakers needed for immersion. Advanced setups use omnidirectional capsules (e.g., DPA 4060) for high-fidelity capture, often in silicone ears to replicate natural diffraction. I use the 3DIO Microphones today but I would cover a dummy head in texture material and place two stereo (4 channels) microphones in each ear. I would then mix down the resulting signals into stereo. The 3DIO series features dual omnidirectional capsules in realistic silicone ear molds, spaced 14 cm apart for compact, accurate 3D capture based on over 13 years of research into human hearing. Models like the Free Space Pro II use premium DPA 4060 CORE capsules for ultra-low noise and high sensitivity, delivering stereo output ideal for immersive applications like game audio. Today just about any ASMR producer uses these. But I use them to capture, curate and archive sound and video we will lose or just about lost for AI training in a way no model or AI company is doing today. I am duplicating the human 3D binaural audio experience and memory. Below is a crude demonstration. If you can listen in headphones. Or turn your phone sideways to feel the audio space. I’ll have a far more professional demo soon to show the real power of 3D audio. (Oh that music is a MIDI player that uses disks to play).

Brian Roemmele

30,800 görüntüleme • 7 ay önce

Biggest announcement in company history. Here it goes: Platter+ is now live on the Shopify App Store and free to start. Platter+ lets you optimize your checkout and post-purchase without needing a designer or developer. It takes minutes to set up and start driving additional revenue: → Download the app directly to your Shopify admin → Select and configure pre-built checkout and post-purchase extensions → Turn them on and see the conversion rate and AOV increase The checkout and post-purchase experiences are often overlooked, but they’re the simplest and quickest way to drive more dollars from existing shoppers. We’ve worked with hundreds of brands to address high checkout abandonment rates, struggling conversion rates, and low AOV. All of that has been built directly into this product. You spoke, and we listened. Brands are tired of usage-based pricing. It’s unpredictable and feels like a tax on success. That’s why we chose a flat fee pricing model. It’s simple: you pay the same amount, whether you generate $1,000, $100,000, or $1,000,000. Over 150 of our customers have been using the app for months to drive incremental sales. Most merchants see a sales lift minutes after going live. If you’ve read this far, we want to make it easier for you. We built the most extensive playbook on checkout optimization, period. 100+ pages, 15 partners, Shopify-endorsed. We’ll give it to you for free. Here’s how to get it: → You MUST connect with me on LinkedIn (I can’t send it otherwise) → Like this same post on LinkedIn and comment “Checkout” I’ll DM it to you. To sweeten the deal, our team will build you an optimized checkout experience in a 15-minute meeting. If you’re interested, message me, and I’ll ensure you get taken care of.
2:05

Biggest announcement in company history. Here it goes: Platter+ is now live on the Shopify App Store and free to start. Platter+ lets you optimize your checkout and post-purchase without needing a designer or developer. It takes minutes to set up and start driving additional revenue: → Download the app directly to your Shopify admin → Select and configure pre-built checkout and post-purchase extensions → Turn them on and see the conversion rate and AOV increase The checkout and post-purchase experiences are often overlooked, but they’re the simplest and quickest way to drive more dollars from existing shoppers. We’ve worked with hundreds of brands to address high checkout abandonment rates, struggling conversion rates, and low AOV. All of that has been built directly into this product. You spoke, and we listened. Brands are tired of usage-based pricing. It’s unpredictable and feels like a tax on success. That’s why we chose a flat fee pricing model. It’s simple: you pay the same amount, whether you generate $1,000, $100,000, or $1,000,000. Over 150 of our customers have been using the app for months to drive incremental sales. Most merchants see a sales lift minutes after going live. If you’ve read this far, we want to make it easier for you. We built the most extensive playbook on checkout optimization, period. 100+ pages, 15 partners, Shopify-endorsed. We’ll give it to you for free. Here’s how to get it: → You MUST connect with me on LinkedIn (I can’t send it otherwise) → Like this same post on LinkedIn and comment “Checkout” I’ll DM it to you. To sweeten the deal, our team will build you an optimized checkout experience in a 15-minute meeting. If you’re interested, message me, and I’ll ensure you get taken care of.

Ben Sharf

18,074 görüntüleme • 1 yıl önce

#3 The prevalence of vegetable oils in processed foods is staggering due to their cost-effectiveness. But how often do you take a moment to read the small print on the back of a product? Here's a simple rule: whenever you spot "vegetable oil" in the ingredients, RUN FOR YOUR LIFE. 💡 Now, let's delve into the history of why vegetable oils became so prevalent a century ago. Back in 1900, an entirely different story was unfolding across the ocean. The German army was actively seeking a synthetic lubricant for diesel engines used in submarines. In 1902, the German chemist Wilhelm Normann achieved a groundbreaking milestone by successfully solidifying vegetable oils. At the same time, the United States was grappling with a surplus of cotton production, leading to a dilemma on how to utilize the waste streams, especially the seeds. Instead of discarding them, someone had the idea to extract oil from these seeds. However, there was a significant hurdle to overcome – the presence of a toxin called gossypol within the cotton seeds. 🔥 To rid the oil of toxins like gossypol found in cotton seeds, a similar refining process was employed, involving high heat, chemicals, and immense pressure. Yet, this process had its own set of problems. Exposure to high heat during refining made the oil prone to oxidation, leading to the accumulation of free radicals, which harm cells and contribute to illness and aging, as explained in a previous post. 🕰️ Around 1920, this product was transformed into something you might recognize today as 'Crisco,' an abbreviation for Crystalized Cottonseed Oil. But eventually, soybean oil emerged as a cheaper alternative. Remember, in the world of business, profits often take precedence over people's health. Watch the whole video about ‘The $100 Billion Dollar Ingredient making your Food Toxic’ here:
4:08

#3 The prevalence of vegetable oils in processed foods is staggering due to their cost-effectiveness. But how often do you take a moment to read the small print on the back of a product? Here's a simple rule: whenever you spot "vegetable oil" in the ingredients, RUN FOR YOUR LIFE. 💡 Now, let's delve into the history of why vegetable oils became so prevalent a century ago. Back in 1900, an entirely different story was unfolding across the ocean. The German army was actively seeking a synthetic lubricant for diesel engines used in submarines. In 1902, the German chemist Wilhelm Normann achieved a groundbreaking milestone by successfully solidifying vegetable oils. At the same time, the United States was grappling with a surplus of cotton production, leading to a dilemma on how to utilize the waste streams, especially the seeds. Instead of discarding them, someone had the idea to extract oil from these seeds. However, there was a significant hurdle to overcome – the presence of a toxin called gossypol within the cotton seeds. 🔥 To rid the oil of toxins like gossypol found in cotton seeds, a similar refining process was employed, involving high heat, chemicals, and immense pressure. Yet, this process had its own set of problems. Exposure to high heat during refining made the oil prone to oxidation, leading to the accumulation of free radicals, which harm cells and contribute to illness and aging, as explained in a previous post. 🕰️ Around 1920, this product was transformed into something you might recognize today as 'Crisco,' an abbreviation for Crystalized Cottonseed Oil. But eventually, soybean oil emerged as a cheaper alternative. Remember, in the world of business, profits often take precedence over people's health. Watch the whole video about ‘The $100 Billion Dollar Ingredient making your Food Toxic’ here:

Dr. Simon

164,810 görüntüleme • 2 yıl önce

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²
41:01

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²

Generation Infinity

170,375 görüntüleme • 7 ay önce

I've been with Firstock since day one, and we’ve worked closely with Vikram, the founder. In 2023, Firstock set out to build the fastest, most user-friendly trading app completely in-house. After countless meetings, iterations, and late nights, I’m proud to present the latest version of the Firstock web and mobile app—designed to transform your trading experience. 🚀 You can open your account here: (Open your account today for exciting offers) Experience Demo Account: Zero Hassle. Zero Charges. Maximum Power. With Firstock, you get: * ₹0 Delivery Charges * ₹0 API Fees * ₹0 Account Opening Charges * ₹0 Pledge Charges * ₹0 AMC * ₹0 Pay-in Charges * Just ₹20/order for F&O trades What do we have? For Investors: Let’s begin with what Firstock offers to investors: 1. Fundamentals at Your Fingertips Access detailed stock charts and fundamental data directly within the app—no need to go elsewhere. 2. Holding Performance Overview Track your portfolio's performance with full visibility into all corporate actions affecting your holdings. 3. Complete Holding Analysis Gain a holistic view of your portfolio through our intuitive holdings dashboard. 4. Instant Pledge for Instant Margin Need margin quickly? Instantly pledge your stocks and start trading within minutes—no delays. Confused about what to invest in? 5. Curated Investment Ideas Explore top-performing market movers, sectoral trends, and international ETFs to make informed investment choices. 6. Custom Screeners Build your own stock screeners to filter out investments that match your strategy and risk appetite. --- For Traders: Built by traders, for traders—our platform is designed to meet your high-speed, high-efficiency needs. 1. Sticky Orders & Bulk Slicing*l Place large orders with ease. Our bulk slicing and sticky order window make placing, modifying, and exiting large quantities seamless. 2. Options Strategy Builder Design strategies directly from the option chain, view the payoff graph, and execute instantly. 3. Live Position Analysis Analyze and tweak your open positions directly from the position book—no switching screens. 4. Custom Strategy Execution Save your favorite strategies and execute them when the timing is right. 5. Advanced Option Analytics View real-time data like OI, Max Pain, and synthetic futures to make quicker, smarter trading decisions. 6. Pre-Built Straddle and Strangle Tools Trade straddles or strangles effortlessly using our dedicated strategy screens. --- Now on Mobile: Enjoy the same powerful features on our brand-new mobile app—designed for ease, speed, and convenience. --- Try it Today: Experience the platform with our demo—explore all features before you commit. Explore Demo Account: You can open your account here: --- This is just the beginning. We’re continuously building features that will redefine the way you trade. Have suggestions or feedback? I’d love to hear from you personally. Let’s grow together.
1:38

I've been with Firstock since day one, and we’ve worked closely with Vikram, the founder. In 2023, Firstock set out to build the fastest, most user-friendly trading app completely in-house. After countless meetings, iterations, and late nights, I’m proud to present the latest version of the Firstock web and mobile app—designed to transform your trading experience. 🚀 You can open your account here: (Open your account today for exciting offers) Experience Demo Account: Zero Hassle. Zero Charges. Maximum Power. With Firstock, you get: * ₹0 Delivery Charges * ₹0 API Fees * ₹0 Account Opening Charges * ₹0 Pledge Charges * ₹0 AMC * ₹0 Pay-in Charges * Just ₹20/order for F&O trades What do we have? For Investors: Let’s begin with what Firstock offers to investors: 1. Fundamentals at Your Fingertips Access detailed stock charts and fundamental data directly within the app—no need to go elsewhere. 2. Holding Performance Overview Track your portfolio's performance with full visibility into all corporate actions affecting your holdings. 3. Complete Holding Analysis Gain a holistic view of your portfolio through our intuitive holdings dashboard. 4. Instant Pledge for Instant Margin Need margin quickly? Instantly pledge your stocks and start trading within minutes—no delays. Confused about what to invest in? 5. Curated Investment Ideas Explore top-performing market movers, sectoral trends, and international ETFs to make informed investment choices. 6. Custom Screeners Build your own stock screeners to filter out investments that match your strategy and risk appetite. --- For Traders: Built by traders, for traders—our platform is designed to meet your high-speed, high-efficiency needs. 1. Sticky Orders & Bulk Slicing*l Place large orders with ease. Our bulk slicing and sticky order window make placing, modifying, and exiting large quantities seamless. 2. Options Strategy Builder Design strategies directly from the option chain, view the payoff graph, and execute instantly. 3. Live Position Analysis Analyze and tweak your open positions directly from the position book—no switching screens. 4. Custom Strategy Execution Save your favorite strategies and execute them when the timing is right. 5. Advanced Option Analytics View real-time data like OI, Max Pain, and synthetic futures to make quicker, smarter trading decisions. 6. Pre-Built Straddle and Strangle Tools Trade straddles or strangles effortlessly using our dedicated strategy screens. --- Now on Mobile: Enjoy the same powerful features on our brand-new mobile app—designed for ease, speed, and convenience. --- Try it Today: Experience the platform with our demo—explore all features before you commit. Explore Demo Account: You can open your account here: --- This is just the beginning. We’re continuously building features that will redefine the way you trade. Have suggestions or feedback? I’d love to hear from you personally. Let’s grow together.

Saketh R

15,618 görüntüleme • 1 yıl önce

what is "retail" and what can be today? it's wild how little attention this space gets tbh retail isn't just about profit margins & sales conversions - it's this fascinating bridge between innovation culture & everyday life, especially in design and fashion but nobody's really cracked it for virtual spaces yet all the worlds by dolce gabbana and others are like a very low effort interfaces that dont talk enough to the real user, it is a marketing move to say we did that, we follow tech bla bla (imo :) spent months researching traditional retail spaces - marble floors + high ceilings + that specific type of lighting that makes everything look expensive + trained staff in perfectly pressed + ironed uniforms - it's all carefully orchestrated then you've got these sleek 2d websites - full screen product shots + bold typography + minimal clicks to checkout - they work but something's missing, that human element that makes physical retail special started building this hybrid concept back in october 23 - imagine a web-based spatial store where your digital twin can actually try stuff on instantly - no more guessing if that jacket fits your avatar retail spaces are like these sacred temples of brand culture - acne stores hit different than zara ones & that's intentional - each space tells a story about what the brand believes in metaverse retail flips this whole concept - instead of walking 20 mins to a store you're literally one click away from being inside this carefully crafted virtual environment - ai npcs that actually understand fashion & can help you find exactly what you're looking for here's the thing about virtual retail - it's not just about pushing products - created this space where you can just vibe, test out avatars, play with different looks - if you buy something cool but the experience matters more. feeling same for the stores, new gen irl stores switched the narrative to a spaces where you can have an espresso and chill these ai npcs are different - they're not following you around like those overeager sales assistants - they're just chilling in the space, ready to help if you need them but totally cool if you just want to explore went through like 12 different iterations of interior design thru 3 years - each version taught something new about how people interact with virtual fashion - it's wild how much user behavior in virtual spaces differs from physical retail realized something big during development - creating the collection isn't even half the battle anymore - the way you present it, the whole experience around it, that's become this massive design challenge it's like gesamtkunstwerk but for the metaverse - every single element needs to be intentional - the lighting, the sound design, the way avatars move through the space - it all matters built this whole ecosystem around the concept - the store connects to the runway experience connects to the website connects to the marketplace - everything flows together serving this bigger vision of what virtual fashion can be this isn't just about selling digital clothes - it's about creating this accessible gateway into 3d internet culture - making virtual fashion something that actually makes sense in people's daily digital lives the lighting system alone took a month - because shadows & reflections hit different in virtual space - needed to make materials look good but also render fast enough for smooth experience on todays low gpu vr machine devices future of retail isn't physical or digital - it's this wild hybrid space where real & virtual blend together - imagine walking into a physical store & seeing your virtual wardrobe projected onto your reflection looking at the data now - users spend avg 23 mins in the virtual store compared to 7 mins on traditional e-commerce sites - they're not just shopping, they're exploring & connecting with the brand story accessibility was key in design - wanted anyone with a decent internet connection to access this space - no fancy vr gear required just your browser & imagination each virtual store could visit generates this unique experience - in future, the space can remember your preferences but also introduces new elements each time - keeps the discovery feeling fresh what's wild is how this changes the whole fashion calendar - virtual retail spaces can transform instantly - new collection drops can completely reshape the environment in seconds - no more seasonal renovations, change the glb. looking ahead this could revolutionize how we think about brand spaces - why limit yourself to physics when your store could literally defy gravity - imagine trying on a jacket while floating through a nebula retail in metaverse isn't just about replicating physical stores - it's about creating these impossible spaces that still somehow feel familiar & welcoming - that's the sweet spot we're all chasing built this thinking about the next wave of digital natives - they're gonna expect these kinds of hybrid experiences - traditional e-commerce gonna feel as outdated as catalog shopping feels to us now the tech's finally catching up to the vision - webgl performance + ai integration + virtual try-on tech all hitting that sweet spot where imagination meets practicality all my thoughts here are quite alpha in sense of applying today, was able to apply only some of them thanks to hyperfy, but the vision is here. breathe it. now with v2, all of these thoughts can be applied and developped. this is just the beginning tbh - every problem solved opens up new possibilities - excited to see how others build on these concepts & push virtual retail even further. fubu side note: made an ai gen notorious big song for it, enjoyy.
6:31

what is "retail" and what can be today? it's wild how little attention this space gets tbh retail isn't just about profit margins & sales conversions - it's this fascinating bridge between innovation culture & everyday life, especially in design and fashion but nobody's really cracked it for virtual spaces yet all the worlds by dolce gabbana and others are like a very low effort interfaces that dont talk enough to the real user, it is a marketing move to say we did that, we follow tech bla bla (imo :) spent months researching traditional retail spaces - marble floors + high ceilings + that specific type of lighting that makes everything look expensive + trained staff in perfectly pressed + ironed uniforms - it's all carefully orchestrated then you've got these sleek 2d websites - full screen product shots + bold typography + minimal clicks to checkout - they work but something's missing, that human element that makes physical retail special started building this hybrid concept back in october 23 - imagine a web-based spatial store where your digital twin can actually try stuff on instantly - no more guessing if that jacket fits your avatar retail spaces are like these sacred temples of brand culture - acne stores hit different than zara ones & that's intentional - each space tells a story about what the brand believes in metaverse retail flips this whole concept - instead of walking 20 mins to a store you're literally one click away from being inside this carefully crafted virtual environment - ai npcs that actually understand fashion & can help you find exactly what you're looking for here's the thing about virtual retail - it's not just about pushing products - created this space where you can just vibe, test out avatars, play with different looks - if you buy something cool but the experience matters more. feeling same for the stores, new gen irl stores switched the narrative to a spaces where you can have an espresso and chill these ai npcs are different - they're not following you around like those overeager sales assistants - they're just chilling in the space, ready to help if you need them but totally cool if you just want to explore went through like 12 different iterations of interior design thru 3 years - each version taught something new about how people interact with virtual fashion - it's wild how much user behavior in virtual spaces differs from physical retail realized something big during development - creating the collection isn't even half the battle anymore - the way you present it, the whole experience around it, that's become this massive design challenge it's like gesamtkunstwerk but for the metaverse - every single element needs to be intentional - the lighting, the sound design, the way avatars move through the space - it all matters built this whole ecosystem around the concept - the store connects to the runway experience connects to the website connects to the marketplace - everything flows together serving this bigger vision of what virtual fashion can be this isn't just about selling digital clothes - it's about creating this accessible gateway into 3d internet culture - making virtual fashion something that actually makes sense in people's daily digital lives the lighting system alone took a month - because shadows & reflections hit different in virtual space - needed to make materials look good but also render fast enough for smooth experience on todays low gpu vr machine devices future of retail isn't physical or digital - it's this wild hybrid space where real & virtual blend together - imagine walking into a physical store & seeing your virtual wardrobe projected onto your reflection looking at the data now - users spend avg 23 mins in the virtual store compared to 7 mins on traditional e-commerce sites - they're not just shopping, they're exploring & connecting with the brand story accessibility was key in design - wanted anyone with a decent internet connection to access this space - no fancy vr gear required just your browser & imagination each virtual store could visit generates this unique experience - in future, the space can remember your preferences but also introduces new elements each time - keeps the discovery feeling fresh what's wild is how this changes the whole fashion calendar - virtual retail spaces can transform instantly - new collection drops can completely reshape the environment in seconds - no more seasonal renovations, change the glb. looking ahead this could revolutionize how we think about brand spaces - why limit yourself to physics when your store could literally defy gravity - imagine trying on a jacket while floating through a nebula retail in metaverse isn't just about replicating physical stores - it's about creating these impossible spaces that still somehow feel familiar & welcoming - that's the sweet spot we're all chasing built this thinking about the next wave of digital natives - they're gonna expect these kinds of hybrid experiences - traditional e-commerce gonna feel as outdated as catalog shopping feels to us now the tech's finally catching up to the vision - webgl performance + ai integration + virtual try-on tech all hitting that sweet spot where imagination meets practicality all my thoughts here are quite alpha in sense of applying today, was able to apply only some of them thanks to hyperfy, but the vision is here. breathe it. now with v2, all of these thoughts can be applied and developped. this is just the beginning tbh - every problem solved opens up new possibilities - excited to see how others build on these concepts & push virtual retail even further. fubu side note: made an ai gen notorious big song for it, enjoyy.

decentralize*

12,505 görüntüleme • 1 yıl önce

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)
0:51

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

Sam Hogan 🇺🇸

374,577 görüntüleme • 3 yıl önce