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I built a plugin to generate 3D assets directly from Codex, using the latest update that introduces image generation. It generates an image, calls the Tripo3D API, then starts an MCP server and a local viewer directly inside Codex. It uses the paid Tripo3D API (12 free generations, 600...

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Defend Intelligence (Anis Ayari)

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11,983 görüntüleme • 3 ay önce •via X (Twitter)

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the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model
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the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model

Jan

131,884 görüntüleme • 8 ay önce

Working on a “Hand-Off to Mac” way to load the chats from the Codex Remote Control for iOS app to the Codex App Codex app-server doesn’t quite support a live update So I decided to find a workaround A button that forces the closing of the Codex App and reopening it in the chat created from iPhone Would you use this button?
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Working on a “Hand-Off to Mac” way to load the chats from the Codex Remote Control for iOS app to the Codex App Codex app-server doesn’t quite support a live update So I decided to find a workaround A button that forces the closing of the Codex App and reopening it in the chat created from iPhone Would you use this button?

Emanuele Di Pietro

24,351 görüntüleme • 4 ay önce

.Metaplex Genesis is the ignition key for the Internet Capital Markets. We have just handed that key to AI agents. Introducing metaplex-genesis-mcp: an open-source Model Context Protocol server connecting agents directly to the Genesis protocol on Solana. Special thanks to Blockiosaurus🦾🥖 and Tony Boyle for the support. Link below.
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.Metaplex Genesis is the ignition key for the Internet Capital Markets. We have just handed that key to AI agents. Introducing metaplex-genesis-mcp: an open-source Model Context Protocol server connecting agents directly to the Genesis protocol on Solana. Special thanks to Blockiosaurus🦾🥖 and Tony Boyle for the support. Link below.

maikers

10,127 görüntüleme • 7 ay önce

Litter iOS just got a beta (TestFlight) release so now you can install it and try it out. It's a remote for codex! - Finds all the computers on your local network (or tailscale) that run codex app-server or ssh - Lets you continue any session or start a new one - Experimental local codex with oauth support Free, fully open-source, fully native, with android release coming soon! Thanks to Maky drumroll.dev ☄️ PincentΞ for the contributions! TestFlight ⬇️
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Litter iOS just got a beta (TestFlight) release so now you can install it and try it out. It's a remote for codex! - Finds all the computers on your local network (or tailscale) that run codex app-server or ssh - Lets you continue any session or start a new one - Experimental local codex with oauth support Free, fully open-source, fully native, with android release coming soon! Thanks to Maky drumroll.dev ☄️ PincentΞ for the contributions! TestFlight ⬇️

SIGKITTEN

68,308 görüntüleme • 4 ay önce

Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.
0:17

Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.

Kritarth Mittal | Soshals

12,248 görüntüleme • 3 ay önce

Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂
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Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂

waishnav

527,704 görüntüleme • 1 ay önce

Every app eventually needs an image viewer. Somehow, most of them still feel janky on the web. I’ve been working on a composable Lightbox primitive - Base UI / Radix by WorkOS / shadcn style API, native-feeling transitions, touch gestures, pinch-to-zoom, keyboard support, and all the little details that make it feel real. Is this something you’d reach for?
0:15

Every app eventually needs an image viewer. Somehow, most of them still feel janky on the web. I’ve been working on a composable Lightbox primitive - Base UI / Radix by WorkOS / shadcn style API, native-feeling transitions, touch gestures, pinch-to-zoom, keyboard support, and all the little details that make it feel real. Is this something you’d reach for?

Artur Bień

25,847 görüntüleme • 14 gün önce

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 görüntüleme • 4 ay önce

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more
0:20

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more

@levelsio

134,777 görüntüleme • 3 ay önce

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
0:12

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,801 görüntüleme • 1 yıl önce

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!
0:14

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

19,931 görüntüleme • 1 ay önce

Codex CLI Update: Let there be Search Whatup nerds, back so soon looking or yet ANOTHER update?! I got you. Update 0.121.0 is here! > You can now search through previous user prompts with CTRL+R. Just trigger search and enter your search string, you can easily arrow through all matches. See video below! > 🥔 Support for Spud! Is not here yet. Sorry. Maybe tomorrow. 🫢 > v0.121.0 adds custom marketplace installs in Codex: you can run codex marketplace add to register marketplaces from GitHub shorthand, git URLs, or local directories. Codex validates the marketplace layout and stores it in your user config so it shows up consistently in plugin discovery. > Improved memory features, including a new /memories TUI menu with use/generate toggles, a reset-all-memories action, app-server support for setting thread memory mode and clearing memories, and cleanup of stale memory-extension resources. Note: Not working on Linux for me. /memories command unavailable. > Codex MCP got further upgrades with direct app tool calls, cleaner namespacing, and safe optional parallel execution for faster workflows > Codex realtime got better controls (text/audio + clear “done” signals), easier history syncing, and safer file handling. > Hardened devcontainer setup plus smarter macOS socket allowlists for safer local runtime access. > Dozens of other bug fixes, see repo below. Toodles! ✌️
0:15

Codex CLI Update: Let there be Search Whatup nerds, back so soon looking or yet ANOTHER update?! I got you. Update 0.121.0 is here! > You can now search through previous user prompts with CTRL+R. Just trigger search and enter your search string, you can easily arrow through all matches. See video below! > 🥔 Support for Spud! Is not here yet. Sorry. Maybe tomorrow. 🫢 > v0.121.0 adds custom marketplace installs in Codex: you can run codex marketplace add to register marketplaces from GitHub shorthand, git URLs, or local directories. Codex validates the marketplace layout and stores it in your user config so it shows up consistently in plugin discovery. > Improved memory features, including a new /memories TUI menu with use/generate toggles, a reset-all-memories action, app-server support for setting thread memory mode and clearing memories, and cleanup of stale memory-extension resources. Note: Not working on Linux for me. /memories command unavailable. > Codex MCP got further upgrades with direct app tool calls, cleaner namespacing, and safe optional parallel execution for faster workflows > Codex realtime got better controls (text/audio + clear “done” signals), easier history syncing, and safer file handling. > Hardened devcontainer setup plus smarter macOS socket allowlists for safer local runtime access. > Dozens of other bug fixes, see repo below. Toodles! ✌️

am.will

21,842 görüntüleme • 3 ay önce

Character Consistency with Google Veo 3 now in Gemini API! 🤯 Use Images as starting frame to keep character consistency! Here is an python script on how to make consistent viral videos, like you see on TikTok or Youtube shorts: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. Generates a Image based on the first scene using Imagen 3 3. For each scene prompt Veo 3 (fast) generates a video clip. 4. Uses Gemini 2.0 image editing to make sure the starting images fits the scenes 5. Combine the individual video clips into a single final video using MoviePy Veo 3 starts at $0.75 / second and Veo 3 at $0.40 / second with audio. ! 📹 🔉 Prompt: “A realistic energy drink commercial for athletes.”
0:15

Character Consistency with Google Veo 3 now in Gemini API! 🤯 Use Images as starting frame to keep character consistency! Here is an python script on how to make consistent viral videos, like you see on TikTok or Youtube shorts: 1. Based on an idea, it generates a series of scene prompts using Gemini 2.5. 2. Generates a Image based on the first scene using Imagen 3 3. For each scene prompt Veo 3 (fast) generates a video clip. 4. Uses Gemini 2.0 image editing to make sure the starting images fits the scenes 5. Combine the individual video clips into a single final video using MoviePy Veo 3 starts at $0.75 / second and Veo 3 at $0.40 / second with audio. ! 📹 🔉 Prompt: “A realistic energy drink commercial for athletes.”

Philipp Schmid

15,863 görüntüleme • 11 ay önce

My girlfriend of several years asked me to go ring shopping with her this weekend. I replied, "respectfully babe, i love you, but no." Here's why ↓ When i asked about the motivation behind going, she revealed that it wasn't about finding out the size of her ring (which most girls apparently already know). It was actually to see how each stone size and shape looked on her particular finger. Also, the ring people she wanted to make appointments at were over an hour away. obviously way too far I then remembered that with @windsurf , I could quickly vibe code an API wrapper app that uses an image-to-image model that can take a picture of her hand as an input, and then virtually try-on different stones on her finger. And then as of yesterday, Windsurf enabled App Deploys that would allow me to deploy my api wrapper app to the internet in less than a minute so i could share it with my girlfriend I imagine I'm not the only person going through this situation so you can also visit the site at There's no auth and anyone can use it! I've loaded about $10 into my api account and each run costs me $0.01 so feel free to use it but please don't abuse it lol
0:11

My girlfriend of several years asked me to go ring shopping with her this weekend. I replied, "respectfully babe, i love you, but no." Here's why ↓ When i asked about the motivation behind going, she revealed that it wasn't about finding out the size of her ring (which most girls apparently already know). It was actually to see how each stone size and shape looked on her particular finger. Also, the ring people she wanted to make appointments at were over an hour away. obviously way too far I then remembered that with @windsurf , I could quickly vibe code an API wrapper app that uses an image-to-image model that can take a picture of her hand as an input, and then virtually try-on different stones on her finger. And then as of yesterday, Windsurf enabled App Deploys that would allow me to deploy my api wrapper app to the internet in less than a minute so i could share it with my girlfriend I imagine I'm not the only person going through this situation so you can also visit the site at There's no auth and anyone can use it! I've loaded about $10 into my api account and each run costs me $0.01 so feel free to use it but please don't abuse it lol

Rob

136,841 görüntüleme • 1 yıl önce

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)
0:12

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)

Pounds

17,116 görüntüleme • 3 ay önce

The xAI API is incredible. I just created an AI assistant that can fetch news content from URLs and write a post about it on my own writing style. Super simple to set up and you can try free. Here’s how:
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The xAI API is incredible. I just created an AI assistant that can fetch news content from URLs and write a post about it on my own writing style. Super simple to set up and you can try free. Here’s how:

Alvaro Cintas

15,384,417 görüntüleme • 1 yıl önce

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.
0:18

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.

Axel Bitblaze 🪓

47,734 görüntüleme • 25 gün önce

I'm still playing with 3D storyboards for Seedance 2.0. Even when I ask for a 2D video, using a 3D storyboard often pushes the model toward a 3D look. I haven't tested it extensively yet but there definitely seems to be a bias. So 2D storyboards work better for 2D videos, while 3D storyboards are better suited for 3D or photorealistic outputs. Another thing I found is that my video prompt is already very detailed. If I remove the storyboard section and generate the video without attaching a storyboard, the model still follows the same beats. The main difference is the environment. With a storyboard, it tends to recreate an environment that closely matches the storyboard. I also think storyboards help with certain poses and compositions. If you're curious about the prompts, I've included them below.
0:15

I'm still playing with 3D storyboards for Seedance 2.0. Even when I ask for a 2D video, using a 3D storyboard often pushes the model toward a 3D look. I haven't tested it extensively yet but there definitely seems to be a bias. So 2D storyboards work better for 2D videos, while 3D storyboards are better suited for 3D or photorealistic outputs. Another thing I found is that my video prompt is already very detailed. If I remove the storyboard section and generate the video without attaching a storyboard, the model still follows the same beats. The main difference is the environment. With a storyboard, it tends to recreate an environment that closely matches the storyboard. I also think storyboards help with certain poses and compositions. If you're curious about the prompts, I've included them below.

Kōda

40,627 görüntüleme • 18 gün önce

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch
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🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,481 görüntüleme • 1 yıl önce