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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... show more

Defend Intelligence (Anis Ayari)

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12,151 Aufrufe • vor 4 Monaten •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

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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.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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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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SIGKITTEN

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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.

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waishnav

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K2S

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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.

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Akshay 🚀

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@levelsio

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MIKE

74,617 Aufrufe • vor 16 Tagen

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!

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!

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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.

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

52,849 Aufrufe • vor 1 Jahr

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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

22,020 Aufrufe • vor 4 Monaten

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▣ Introducing Endless: infinite inference (kinda). An experimental harness to milk every ounce out of your Codex subscription. Since Codex can let an in-progress turn keep going even after your usage hits 100%, why not put that to the test? Endless starts one Codex turn and gives the agent a wait_for_user_input tool. Once it finishes a task, it calls that tool and waits. Your next message becomes the tool result, keeping the entire session inside the same turn. It runs through Codex’s own app server using your existing ChatGPT login. Native tools, automatic compaction, context tracking, and quota tracking still work as usual. ⚠️ NOTE: I CAN’T CONFIRM THAT YOU WON’T GET BANNED OR PUNISHED FOR USING THIS TOOL. USE IT AT YOUR OWN RISK.

maria

254,287 Aufrufe • vor 16 Tagen

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

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 Aufrufe • vor 1 Jahr

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)

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

17,241 Aufrufe • vor 5 Monaten

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:

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

15,385,478 Aufrufe • vor 1 Jahr