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D2 is a diagram scripting language that turns plain text into architecture diagrams. - CLI tool that renders `.d2` files to SVGs with live-reload on save. - Supports multiple layout engines like ELK and customizable themes. - Exports to SVG, PNG, PDF, and other formats. - Includes a web... show more

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THIS GUY VIBE CODED A FREE TOOL THAT TURNS UGLY BARCODES INTO BEAUTIFUL SVG ONES THAT STILL SCAN you paste in a barcode, and it turns it into a styled, artistic SVG that still scans perfectly the tool makes it look good AND keeps it scannable free, no account needed, AND they export as SVGs if you've ever looked at a barcode on your product packaging and thought "this is ugly" you might want to look into this

THIS GUY VIBE CODED A FREE TOOL THAT TURNS UGLY BARCODES INTO BEAUTIFUL SVG ONES THAT STILL SCAN you paste in a barcode, and it turns it into a styled, artistic SVG that still scans perfectly the tool makes it look good AND keeps it scannable free, no account needed, AND they export as SVGs if you've ever looked at a barcode on your product packaging and thought "this is ugly" you might want to look into this

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Introducing SVG Arena! SVGs are XML-based text files that encode geometry, and they're surprisingly tough for state-of-the-art LLMs to get right Here's how bad models (still) are at drawing a pelican on a bicycle - creds to Simon Willison Leaderboard coming soon! In the meantime, here are some recent generations

Introducing SVG Arena! SVGs are XML-based text files that encode geometry, and they're surprisingly tough for state-of-the-art LLMs to get right Here's how bad models (still) are at drawing a pelican on a bicycle - creds to Simon Willison Leaderboard coming soon! In the meantime, here are some recent generations

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Yesterday I made a rather risky decision with my OpenClaw🦞 assistant... I gave her a voice, a Bluetooth speaker and told her to wake me up with a song of her choice I can only say that it wasn't a pleasant experience, especially because she downloaded a YT video that had a studio bumper that sounded like a track from Stranger Things 👹 this is my life now She used a CLI tool to download audio files from any youtube file! The song that made me anxious today: The CLI tool for youtube:

Yesterday I made a rather risky decision with my OpenClaw🦞 assistant... I gave her a voice, a Bluetooth speaker and told her to wake me up with a song of her choice I can only say that it wasn't a pleasant experience, especially because she downloaded a YT video that had a studio bumper that sounded like a track from Stranger Things 👹 this is my life now She used a CLI tool to download audio files from any youtube file! The song that made me anxious today: The CLI tool for youtube:

anita

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A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

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ALCHEMIST AI 🔮

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How I use Claude Code and Remotion to make animated diagrams. Sorry, it's not a single prompt. 1. Find an input language the model knows well. For example, Mermaid for flowcharts. Claude writes it fluently, so it's my entry point. 2. Use Claude to build components that take that input and bake in the guardrails: design system, animation patterns, layout rules. 3. Now I can describe what I want in plain English e.g. "create a flowchart for the tier check section in the script", and Claude translates it to our input language: ``` flowchart TD t1[Tier 1 read-only] t2[Tier 2 in-project writes] t3[Tier 3 everything else] action[Action] --> t1 action --> t2 action --> t3 t1 --> skip([Skips classifier]) t2 --> skip t3 --> classifier{Classifier} classifier --> approve[Approve] classifier --> deny[Deny] ``` The component handles the rest: layout, styling, node and edge reveals. It also takes events for follow-ups like the trace dot that follows a path and lights up nodes. 4. To finish it off, I wrapped the board in a separate CRT shader component. It really helps to have a shared vocabulary with your agent. When I say "rise in fast on enter", it knows I mean fade in while translating up, from a set offset, faster than the default duration, with a specific bezier curve. For common language inspo: look into Matt Pocock `/grill-with-docs` and by Emil Kowalski and Glenn Hitchcock.

How I use Claude Code and Remotion to make animated diagrams. Sorry, it's not a single prompt. 1. Find an input language the model knows well. For example, Mermaid for flowcharts. Claude writes it fluently, so it's my entry point. 2. Use Claude to build components that take that input and bake in the guardrails: design system, animation patterns, layout rules. 3. Now I can describe what I want in plain English e.g. "create a flowchart for the tier check section in the script", and Claude translates it to our input language: ``` flowchart TD t1[Tier 1 read-only] t2[Tier 2 in-project writes] t3[Tier 3 everything else] action[Action] --> t1 action --> t2 action --> t3 t1 --> skip([Skips classifier]) t2 --> skip t3 --> classifier{Classifier} classifier --> approve[Approve] classifier --> deny[Deny] ``` The component handles the rest: layout, styling, node and edge reveals. It also takes events for follow-ups like the trace dot that follows a path and lights up nodes. 4. To finish it off, I wrapped the board in a separate CRT shader component. It really helps to have a shared vocabulary with your agent. When I say "rise in fast on enter", it knows I mean fade in while translating up, from a set offset, faster than the default duration, with a specific bezier curve. For common language inspo: look into Matt Pocock `/grill-with-docs` and by Emil Kowalski and Glenn Hitchcock.

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Building with Leo, Aleo’s DSL, is easier than you think. Leo is a high-level programming language (like Rust or Javascript) that compiles to an intermediate representation (Aleo Instructions) and then straight into a ZK circuit. No in-depth cryptography knowledge needed.

Building with Leo, Aleo’s DSL, is easier than you think. Leo is a high-level programming language (like Rust or Javascript) that compiles to an intermediate representation (Aleo Instructions) and then straight into a ZK circuit. No in-depth cryptography knowledge needed.

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Ideogram 4.0 is now live on Leonardo. As a day 0 launch partner, we’re bringing Ideogram’s most capable text-to-image model to Leonardo. 2K resolution. Accurate text. Precise composition control. This tool was built for designers and marketers who need production-ready assets that actually work. Try out Ideogram 4.0 today and see how this tool can help you bring your ideas to life like never before.

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I'm deleting every paid document extraction tool because of this. Zipstack built Unstract and it turns any PDF, scan, or image into clean structured JSON using LLMs you already pay for. You point it at a document (an invoice, a bank statement, a KYC form, a tax return, a claims form) and it automatically pulls the fields you asked for and hands back a JSON object ready to drop straight into your database. The difference from every other document extraction tool is the setup. Most tools want a model per vendor or regex per template. This works from a single natural language prompt that handles every variation. → Extracts structured JSON from PDFs, scans, and images → Lets you write extraction schemas in plain English through Prompt Studio → Deploys as a REST API or ETL pipeline in minutes → Ships an MCP server so Claude and other agents can extract documents directly → Connects to S3, GCS, Snowflake, BigQuery, Postgres, and every major destination → LLM-agnostic: bring OpenAI, Anthropic, Bedrock, Gemini, Mistral, Ollama, or any provider you already use Basically: you have thousands of documents nobody has time to read. Unstract turns your documents into clean, structured JSON that you can run in production. This is what document extraction should have been all along.

I'm deleting every paid document extraction tool because of this. Zipstack built Unstract and it turns any PDF, scan, or image into clean structured JSON using LLMs you already pay for. You point it at a document (an invoice, a bank statement, a KYC form, a tax return, a claims form) and it automatically pulls the fields you asked for and hands back a JSON object ready to drop straight into your database. The difference from every other document extraction tool is the setup. Most tools want a model per vendor or regex per template. This works from a single natural language prompt that handles every variation. → Extracts structured JSON from PDFs, scans, and images → Lets you write extraction schemas in plain English through Prompt Studio → Deploys as a REST API or ETL pipeline in minutes → Ships an MCP server so Claude and other agents can extract documents directly → Connects to S3, GCS, Snowflake, BigQuery, Postgres, and every major destination → LLM-agnostic: bring OpenAI, Anthropic, Bedrock, Gemini, Mistral, Ollama, or any provider you already use Basically: you have thousands of documents nobody has time to read. Unstract turns your documents into clean, structured JSON that you can run in production. This is what document extraction should have been all along.

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We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: Code: Biological systems are capable of rapid adaptation, given limited sensory cues. For example, our human visual system can quickly adapt and tune its light sensitivity to our surroundings. While modern LLMs exhibit a wide variety of capabilities and knowledge, they remain rigid when adding task-specific capabilities. Traditionally, customizing these models requires gathering large datasets and performing often expensive, time-consuming fine-tuning for specific applications. To bypass these limitations, Text-to-LoRA (T2L) meta-learns a “hypernetwork” that takes in a text description of a desired task, as a prompt, and generates a task-specific LoRA that performs well on the task. In our experiments, we show that T2L can encode hundreds of existing LoRA adapters. While the compression is lossy, T2L maintains the performance of task-specifically tuned LoRA adapters. We also show that T2L can even generalize to unseen tasks given a natural language description of the tasks. Importantly, Text-to-LoRA is parameter-efficient. It generates LoRAs in a single, inexpensive step, based solely on a simple text description of the task. This approach is a step towards dramatically lowering the technical and computational barriers, allowing non-technical users to specialize foundation models using plain language, rather than needing deep technical expertise or large compute resources.

We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025! Paper: Code: Biological systems are capable of rapid adaptation, given limited sensory cues. For example, our human visual system can quickly adapt and tune its light sensitivity to our surroundings. While modern LLMs exhibit a wide variety of capabilities and knowledge, they remain rigid when adding task-specific capabilities. Traditionally, customizing these models requires gathering large datasets and performing often expensive, time-consuming fine-tuning for specific applications. To bypass these limitations, Text-to-LoRA (T2L) meta-learns a “hypernetwork” that takes in a text description of a desired task, as a prompt, and generates a task-specific LoRA that performs well on the task. In our experiments, we show that T2L can encode hundreds of existing LoRA adapters. While the compression is lossy, T2L maintains the performance of task-specifically tuned LoRA adapters. We also show that T2L can even generalize to unseen tasks given a natural language description of the tasks. Importantly, Text-to-LoRA is parameter-efficient. It generates LoRAs in a single, inexpensive step, based solely on a simple text description of the task. This approach is a step towards dramatically lowering the technical and computational barriers, allowing non-technical users to specialize foundation models using plain language, rather than needing deep technical expertise or large compute resources.

Sakana AI

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ok anything ai is actually insane it just one shot this app that scrapes google maps for local business email leads i just took a n8n json file and told anything to build a web app based on this logic it one shot a functioning web app with like 3 databases and web scraping this is actually crazy

ok anything ai is actually insane it just one shot this app that scrapes google maps for local business email leads i just took a n8n json file and told anything to build a web app based on this logic it one shot a functioning web app with like 3 databases and web scraping this is actually crazy

Cody Schneider

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I am able to use ocrmypdf ( to add a text layer to the BIHAR SIR files that have been converted by ECI from digital to unreadable-by-machine in the age of digital. This solution can be run free of cost on your computer, takes a while to convert if you have an old computer, but it allows decent amount of searching. The command I used was: parallel --tag -j 2 ocrmypdf -l hin+eng --optimize 3 --deskew --rotate-pages --clean '{}' 'output/{}' ::: *.pdf You'll have to install both english and hindi language files for tesseract.

I am able to use ocrmypdf ( to add a text layer to the BIHAR SIR files that have been converted by ECI from digital to unreadable-by-machine in the age of digital. This solution can be run free of cost on your computer, takes a while to convert if you have an old computer, but it allows decent amount of searching. The command I used was: parallel --tag -j 2 ocrmypdf -l hin+eng --optimize 3 --deskew --rotate-pages --clean '{}' 'output/{}' ::: *.pdf You'll have to install both english and hindi language files for tesseract.

Pratik Sinha

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Magic Quill: A Free AI Image Editor That Feels Like Magic This tool is incredibly useful and free to use now! It lets you select parts of an image and edit them with just a text prompt. Mostly I’ve gotten exactly what I needed on the first try! Here’s how to use it:

el.cine

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Rive text is live!! Animatable, variable, dynamic text for runtime, with full support for any language. There are a MONSTER amount of new things to play around with! 🚀 Create Text objects, styles, runs, and modifiers. 🚀 Support for any language. LTR, RTL, special characters/features, ligatures, etc. 🚀 Animate variable fonts. 🚀 Google Fonts are provided by default, but you can upload custom fonts. 🚀 Runs are a way to segment a text field into different sections. You can target runs at runtime to change their content dynamically. See documentation here: 🚀 Apply different text styles to runs. 🚀 Convert text to paths (flatten). 🚀 Text modifiers are a powerful way to add layered animation to text, while maintaining its editability! 🚀 Use transform constraints to resize graphics to text objects. Coming soon: 🚧 Ability to export fonts out of band or via CDN. They are currently packaged in the .riv file.

Rive text is live!! Animatable, variable, dynamic text for runtime, with full support for any language. There are a MONSTER amount of new things to play around with! 🚀 Create Text objects, styles, runs, and modifiers. 🚀 Support for any language. LTR, RTL, special characters/features, ligatures, etc. 🚀 Animate variable fonts. 🚀 Google Fonts are provided by default, but you can upload custom fonts. 🚀 Runs are a way to segment a text field into different sections. You can target runs at runtime to change their content dynamically. See documentation here: 🚀 Apply different text styles to runs. 🚀 Convert text to paths (flatten). 🚀 Text modifiers are a powerful way to add layered animation to text, while maintaining its editability! 🚀 Use transform constraints to resize graphics to text objects. Coming soon: 🚧 Ability to export fonts out of band or via CDN. They are currently packaged in the .riv file.

Guido Rosso

25,950 просмотров • 3 лет назад

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

Ihtesham Ali

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Big moment for text-to-speech. Qwen just open-sourced a text-to-speech model that lets you clone voices, design new ones, and control speech using natural language. Let me explain what I mean: You can literally tell it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed. What makes this special: - 3-second voice cloning - Covers 10 languages: English, German, French, and more - Latency as low as 97ms for real-time applications - Supports both streaming and non-streaming generation The model comes in two sizes (0.6B and 1.7B parameters), so you can pick based on your hardware and quality needs. Three modes to work with: 1. Custom Voice: Use pre-built premium voices with instruction-based style control 2. Voice Design: Describe the voice you want in plain English (or Chinese), and the model creates it 3. Voice Clone: Provide a 3-second reference audio and clone that voice The best part? It integrates with vLLM for production deployment and has a simple Python package you can pip install. I've shared a link to the GitHub repo in the next tweet.

Big moment for text-to-speech. Qwen just open-sourced a text-to-speech model that lets you clone voices, design new ones, and control speech using natural language. Let me explain what I mean: You can literally tell it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed. What makes this special: - 3-second voice cloning - Covers 10 languages: English, German, French, and more - Latency as low as 97ms for real-time applications - Supports both streaming and non-streaming generation The model comes in two sizes (0.6B and 1.7B parameters), so you can pick based on your hardware and quality needs. Three modes to work with: 1. Custom Voice: Use pre-built premium voices with instruction-based style control 2. Voice Design: Describe the voice you want in plain English (or Chinese), and the model creates it 3. Voice Clone: Provide a 3-second reference audio and clone that voice The best part? It integrates with vLLM for production deployment and has a simple Python package you can pip install. I've shared a link to the GitHub repo in the next tweet.

Akshay 🚀

31,249 просмотров • 6 месяцев назад

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. Paper Title: MindDrive: A Vision-Language-Action Model for Autonomous Driving via Project: Link:

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. Paper Title: MindDrive: A Vision-Language-Action Model for Autonomous Driving via Project: Link:

AI Bites | YouTube Channel

43,496 просмотров • 5 месяцев назад