Blooming – Chain AI models on a whiteboard to turn text→images→video
Hey HN, I built Blooming, a visual AI workspace where you drag-and-drop “nodes” (text, image, video) onto a whiteboard and chain AI text, image and video models together. It's kinda like n8n but for AI art: • Node-based canvas • Multi-model switching (test different models side by side and iterate multiple versions) • Pipe text or image output to other nodes to refine prompts, turn them into videos or explain images Demo link: https://youtu.be/TdFzhxeRFNg I’d love your feedback on UI clarity, performance, and which models to integrate next. What’s confusing or missing for AI…
What it does
In the maker’s words, at launch
Hey HN, I built Blooming, a visual AI workspace where you drag-and-drop “nodes” (text, image, video) onto a whiteboard and chain AI text, image and video models together. It's kinda like n8n but for AI art: • Node-based canvas • Multi-model switching (test different models side by side and iterate multiple versions) • Pipe text or image output to other nodes to refine prompts, turn them into videos or explain images Demo link: https://youtu.be/TdFzhxeRFNg I’d love your feedback on UI clarity, performance, and which models to integrate next. What’s confusing or missing for AI image and video power users? Everything here is live in prod (you can sign in to use it). Trying to see if this is useful to people. For those interested in the tech stack I used see comment below Thanks for checking it out – Edrick
Does the same job
all alternatives →

Notebooks - Your AI Whiteboard2025 · ▲405Turn YouTube videos, docs & websites into content that wins
- VIVisual inference exploration and experimentation playground2024 · github.com · ▲46
Most inference UIs that I've come across pretty much just give us a chat-like interface to toy around with models in a single visual conversation thread. Given the fact that we are limited to seeing only one output at a time, it's kind of hard to compare outputs from different models, adjustments made to the prompting, and sampler settings. But even when keeping the generation parameters the same (e.g., to test for reliability in the output) and just going for multiple passes, there is no easy way to have a side-by-side comparison to keep track of the outputs from the multiple "rounds". I…

- IBI built an Free AI tool to generate pixel art from text descriptions2025 · pixelateimage.org · ▲21
Hi HN, I'm excited to share a project I've built: PixelateImage.org. Instead of being a traditional image editor, this is an *AI-powered tool that generates pixel art directly from your text prompts.* If you've ever wanted to create unique pixel art characters, scenes, or assets for games, art projects, or just for fun, but perhaps lacked the specific artistic skills or time, this might be for you. My goal was to make generating retro-style or modern pixel art more accessible. You can type in a description (e.g., "a brave knight in a dark forest," "a futuristic cityscape at night," "a cute…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
- UC
Life & fun · 2025 · filiph.github.io
- IB
https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com


