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Say hello to text-to-cad, an open source tool for generating 3D models in Codex! Prompt and edit complex 3D models. Export STEP, STL, GLB, DXF and URDF files. Built for CAD newbies. Link to github below. CAD is hard. As a software engineer getting back into robotics, I’ve been...

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Jake Fitzgerald

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52,444 görüntüleme • 3 ay önce •via X (Twitter)

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KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)
1:12

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

marcus

31,238 görüntüleme • 18 gün önce

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)
1:58

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)

CJ Zafir

39,716 görüntüleme • 1 ay önce

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:
4:17

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

Andrew Ng

151,198 görüntüleme • 2 yıl önce

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting
0:38

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Martin Nebelong

280,005 görüntüleme • 1 yıl önce

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!
4:14

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,842 görüntüleme • 2 yıl önce

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!
1:20

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 görüntüleme • 3 ay önce

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.
2:37

This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.

Chris Long

13,185 görüntüleme • 2 yıl önce

English is literally the hottest programming language. It's absolutely crazy that you can build a complete product in plain English. I'm using Rocket in this video. This is a new app. You type what you want, and Rocket builds it for you. This is great if you want to build: • A landing page • A complete web application • A mobile app • A dashboard to showcase something • An internal tool to automate anything This is another example of how developers should become comfortable being copilots for AI agents (instead of using AI as a copilot). Some of the things I like about Rocket: 1. One-click deploy to Netlify (I use Netlify for all my projects) 2. GitHub integration 3. Supabase integration for your backend 4. Built-in support for Stripe 5. Built-in support for Resend 6. Google Analytics integration 7. Smart AI search with Perplexity 8. You can also integrate with GPT models, Gemini, and Claude 9. You can make visual edits by describing what you want 10. You can upload images and have Rocket implement them Best of all are the templates: They have a large library of templates that will help you get started. These templates will help you save money (because of fewer tokens), and you can modify them as you see fit. I read on the site that you can also bring a Figma file and turn it into an app, but I didn't test that feature. By the way, you can start using it for free. Here is the link: Thanks to the Rocket team for their support and for collaborating with me on this post.
1:11

English is literally the hottest programming language. It's absolutely crazy that you can build a complete product in plain English. I'm using Rocket in this video. This is a new app. You type what you want, and Rocket builds it for you. This is great if you want to build: • A landing page • A complete web application • A mobile app • A dashboard to showcase something • An internal tool to automate anything This is another example of how developers should become comfortable being copilots for AI agents (instead of using AI as a copilot). Some of the things I like about Rocket: 1. One-click deploy to Netlify (I use Netlify for all my projects) 2. GitHub integration 3. Supabase integration for your backend 4. Built-in support for Stripe 5. Built-in support for Resend 6. Google Analytics integration 7. Smart AI search with Perplexity 8. You can also integrate with GPT models, Gemini, and Claude 9. You can make visual edits by describing what you want 10. You can upload images and have Rocket implement them Best of all are the templates: They have a large library of templates that will help you get started. These templates will help you save money (because of fewer tokens), and you can modify them as you see fit. I read on the site that you can also bring a Figma file and turn it into an app, but I didn't test that feature. By the way, you can start using it for free. Here is the link: Thanks to the Rocket team for their support and for collaborating with me on this post.

Santiago

30,944 görüntüleme • 1 yıl önce

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)
0:55

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,769 görüntüleme • 10 ay önce

just dropped a 24-min walkthrough showing how I turn raw n8n automations into actual front-end apps that your team can use no clunky JSON files, no ugly interfaces, and no dev team required I built a clean UI for one of my n8n flows - start to finish - all in one session it’s not a polished SaaS - but it is a working MVP you can test, share, or hand off step-by-step: – mapped the UX in ChatGPT – built the UI live in Bolt – connected it to the backend automation by the end, I had a usable internal tool that felt like a real product — all without writing a single line of frontend code if you're building powerful automations but not wrapping them in UI… you’re missing half the value also dropped my full prompt stack for planning and generating UIs with Claude or ChatGPT want the prompts? comment “UI”, follow + repost and I’ll send them over (must be following so I can DM)
24:30

just dropped a 24-min walkthrough showing how I turn raw n8n automations into actual front-end apps that your team can use no clunky JSON files, no ugly interfaces, and no dev team required I built a clean UI for one of my n8n flows - start to finish - all in one session it’s not a polished SaaS - but it is a working MVP you can test, share, or hand off step-by-step: – mapped the UX in ChatGPT – built the UI live in Bolt – connected it to the backend automation by the end, I had a usable internal tool that felt like a real product — all without writing a single line of frontend code if you're building powerful automations but not wrapping them in UI… you’re missing half the value also dropped my full prompt stack for planning and generating UIs with Claude or ChatGPT want the prompts? comment “UI”, follow + repost and I’ll send them over (must be following so I can DM)

J.B.

58,205 görüntüleme • 1 yıl önce

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!
0:29

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

Onur Solmaz

115,538 görüntüleme • 3 ay önce

I made tons of money building websites for people and small companies. Most of that is now gone. You can now generate, customize, and deploy a website using natural language. I recorded myself building a site from scratch using 10Web.io. 4 minutes later, I had a full website generated just for me. Crazy! That's mostly how I paid rent when I graduated college: I'd either build you a new site or make changes to an existing one. It's nuts anyone can do that today by just asking. But this doesn't stop with generating a website. 10Web also released an AI Copilot that you can use to edit a website with a chat-based AI assistant. Here is what you can do with the AI Copilot: 1. You can edit individual elements and widgets on your site by selecting them and describing the changes you want. 2. You can generate new images, create sections, and generate or edit text. 3. You can also add any new elements to the site. The best way to understand how much progress we've made is by looking at the AI Copilot in action: And the funny part is that we are just figuring out how to use AI models. 2025 will be crazy.
4:17

I made tons of money building websites for people and small companies. Most of that is now gone. You can now generate, customize, and deploy a website using natural language. I recorded myself building a site from scratch using 10Web.io. 4 minutes later, I had a full website generated just for me. Crazy! That's mostly how I paid rent when I graduated college: I'd either build you a new site or make changes to an existing one. It's nuts anyone can do that today by just asking. But this doesn't stop with generating a website. 10Web also released an AI Copilot that you can use to edit a website with a chat-based AI assistant. Here is what you can do with the AI Copilot: 1. You can edit individual elements and widgets on your site by selecting them and describing the changes you want. 2. You can generate new images, create sections, and generate or edit text. 3. You can also add any new elements to the site. The best way to understand how much progress we've made is by looking at the AI Copilot in action: And the funny part is that we are just figuring out how to use AI models. 2025 will be crazy.

Santiago

57,917 görüntüleme • 1 yıl önce

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing
1:03

A GUY PUT GPT-LEVEL AI INSIDE A 3D PRINTED ROBOT AND RELEASED THE ENTIRE THING FOR FREE Most people look at a 3D printer and see plastic toys This guy printed a robot with expressive eyes, object tracking, image recognition, support for ChatGPT, Qwen and offline models, then uploaded the STL files, code and hardware designs for anyone to build themselves The interesting part isn't the robot It's how fast the gap between "I have an idea" and "I built a working product" is disappearing A few years ago this required a team of engineers Now one person with a printer, open-source software and enough curiosity can build something that looks like it came from a robotics startup We're getting very close to a world where manufacturing, AI and robotics stop being separate industries and start becoming the same thing

lagerskoy

57,059 görüntüleme • 1 ay önce

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio
33:22

How to create Farcaster mini app without coding - Step 1: > Go to ChatGPT > Just type: "I would like to create a Farcaster mini app, can you give me some ideas?" > Choose any one of the ideas - Note: - You can also type your own idea to ChatGPT and alter it - Step 2: - Go to ChatGPT > Paste the first prompt (shared in my channel) > Include the project you chose and tell ChatGPT to alter the prompt according to the idea below - Step 3 - Go to: > Sign up using Gmail or GitHub > Projects -> New Project -> Type the name of your project >Just paste the prompt we created earlier > It will start creating your app > If you need to alter anything, just go to GPT and type: [ I need a prompt to alter "your issue (like changing colour, etc.)" in my app already created in v0app by Vercel ] > Copy the prompt and paste it in v0 > No need to worry about errors , it will rectify them for you > If your project is finished, just click "Publish" on your top right > It will automatically host your app in Vercel > Click "Visit site" to see your app on another device and copy the website URL - Step 4 - Go to: v0 app > Just paste Prompt 2 from the file I provided > Click Publish once it’s completed - Step 5 - Go to: v0 app > Just paste the prompt 3 from the file I provided - Step 6 (Important) > Just paste Prompt 4 from the file I provided - Step 7 > Just paste Prompt 4 from the file I provided - Step 8 > Just paste Prompt 6 from the file I provided > It’s an important step to do — you need to create a manifest inside your app so that you can host it on Farcaster - Step 9 - Go to: > Sign up or Login > Settings → Developers → Mini Apps → Create Manifest (New) > Paste your website URL (remove https) > It should be like: > If you find any error while doing this, just copy and paste it in the v0 app > Once everything is finished, you need to create Account Association > Scan the QR on your mobile and tap and hold the Yes button > It will show an error — just copy and paste those errors into the v0 app > Click Publish again, and then click Refresh in Manifest > Click Open your app > for the main prompt file check our tg channel link in our bio

Maran

48,106 görüntüleme • 8 ay önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce

I’ve been playing around with Shortcut to see what I can have AI build for me and the end product is wild. I wanted to stress test Shortcut into building a master excel file for everything I would need for an investment committee. I had it build: 1) A 3-statement model 2) Toggles for multiple cases 3) Charts and tables for IC Memo must haves - revenue by segment, customer concentration, etc. 4) EBITDA Bridge charts 5) A Capital Structure chart You can pretty much have Shortcut model for you while you go and focus on the written parts of the IC Memo. Lmk if you’re interested in learning more, the key here is once you’re a wizard at prompting, the models are incredible.
0:30

I’ve been playing around with Shortcut to see what I can have AI build for me and the end product is wild. I wanted to stress test Shortcut into building a master excel file for everything I would need for an investment committee. I had it build: 1) A 3-statement model 2) Toggles for multiple cases 3) Charts and tables for IC Memo must haves - revenue by segment, customer concentration, etc. 4) EBITDA Bridge charts 5) A Capital Structure chart You can pretty much have Shortcut model for you while you go and focus on the written parts of the IC Memo. Lmk if you’re interested in learning more, the key here is once you’re a wizard at prompting, the models are incredible.

High Yield Harry

34,052 görüntüleme • 2 ay önce

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:
0:31

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

59,803 görüntüleme • 2 ay önce

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.
0:23

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 görüntüleme • 2 yıl önce

A student built a whole faceless passive business by creating AI backyards. I kept seeing these AI backyard builds hitting 50M views and thought it took months of editing, but I was wrong. The creators aren't starting with a dirt lot. They start with the perfect final image and make the AI work backward. And the secret is stupidly simple: generate the final picture first, and let the AI reverse-engineer it. Here is the exact 3-step system you can use to build this: - The Blueprint: Upload your finished backyard to the "Restoration Timelapse" GPT. It reverse-engineers the final image into text prompts for the empty lot. - The Setup: Paste those prompts into Dzine. This generates your "before" images with perfectly matched geometry. - The Animation: Upload your empty lot and finished yard into Kling 3.0 to animate the build. Once Kling spits out the video file, drop it into CapCut, keep the raw construction audio, and export. I broke down the complete, step-by-step architecture with GPT + GROK + CAPCUT in my full guide below 👇
0:39

A student built a whole faceless passive business by creating AI backyards. I kept seeing these AI backyard builds hitting 50M views and thought it took months of editing, but I was wrong. The creators aren't starting with a dirt lot. They start with the perfect final image and make the AI work backward. And the secret is stupidly simple: generate the final picture first, and let the AI reverse-engineer it. Here is the exact 3-step system you can use to build this: - The Blueprint: Upload your finished backyard to the "Restoration Timelapse" GPT. It reverse-engineers the final image into text prompts for the empty lot. - The Setup: Paste those prompts into Dzine. This generates your "before" images with perfectly matched geometry. - The Animation: Upload your empty lot and finished yard into Kling 3.0 to animate the build. Once Kling spits out the video file, drop it into CapCut, keep the raw construction audio, and export. I broke down the complete, step-by-step architecture with GPT + GROK + CAPCUT in my full guide below 👇

Spivach

17,421 görüntüleme • 1 ay önce