Alternatives
Products that do what Federation of robots collaboratively train an object manipulation model does
This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's pretty easy to run, and can do so efficiently if you have access to a recent gaming GPU. Although the diffusion model only take 2GB of VRAM (of course you can decide to scale it up), the compute needed to train them isn't negligible. For context, running the example until…
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- 6IS
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
- 7DA
Hello HN! We have released a major update of our image-to-video diffusion model, DynamiCrafter, with better dynamic, higher resolution, and stronger coherence. DynamiCrafter can animate open-domain still images based on text prompt by leveraging the pre-trained video diffusion priors. Please check our project page and paper for more information. We will continue to improve the model's performance. Comparisons with Stable Video Diffusion and PikaLabs can be found at https://www.youtube.com/watch?v=0NfmIsNAg-g Online demo:…
2024 · github.com
- 8SD
Hey folks, Check out a project I built over the past few days .. a lego mindstorms robot that autonomously drives around a track using vision/neural networks. It was coded entirely in Python. I did a short write-up and would love to get feedback. link: http://slowping.com/2012/self-driving-lego-mindstorms-robot/
2012
- 9HG
Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at…
2025 · youtube.com
- 10UD
Hi HN! I was inspired by Andrej Karpathy's llm.c (https://github.com/karpathy/llm.c), and wrote a full diffusion model training loop in CUDA. I learnt a lot about CUDA from Simon Boehm's Matmul blog (https://siboehm.com/articles/22/CUDA-MMM). Currently there is still a lot of room for optimization: the model is running at 45% speed of PyTorch with torch.compile. I'm curious about any thoughts or CUDA tips for convolutions.
2024 · github.com
- 11S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 12IC
I spent the past week implementing a 1 Layer Neural Net and training it on MNIST within the visual scripting language provided by scratch.mit.edu. It was tedious, but ultimately not too difficult. The code runs incredibly slowly, so much so that 64 samples of MNIST takes 5+ hours to train on my machine. There were a lot of little mini challenges that were fun to overcome (implementing softmax was very tricky). If you're interested, I encourage you to try and improve on it! More details in the linked blog post.
2024 · bell-boy.github.io
- 13HF
We have a massive GPU cluster and developed our own infrastructure to manage the cluster and train massive models. There's how it works: 1. You upload the dataset with preconfigured format into HuggingFaсe [1]. 2. Choose your LLM (e.g. LLaMa 70B, Mistral 7B) 3. Place your submission into the queue 4. Wait for it to get trained. 5. Then you get your trained model there on HuggingFace. Essentially, why would we want to do it? 1. We already have an experience with training big LLMs. 2. We could achieve near-perfect infrastructure performance for training. 3. Sometimes GPUs have just nothing to…
2023 · higgsfield.xyz
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Hi guys, For a few months now I've been working on a web GUI to build, visualise, train and share deep neural models. It's currently reaching a state where opening it for Beta release make sense. Currently the tool support: - Fully connected and Convolutional architecture - Cloud and local, saving / loading of models - Edit / delete layers - Visualise Convolutional layers filters - Freeze / Unfreeze layers - More datasets: Fashion MNIST, QuickDraw(10 and 30) The editor can be found here: https://aifiddle.io. Your feedback, ideas, suggestions are greatly useful, so…
2019
- 15BC
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…
2025 · blooming1000.com
- 16IB
I've been experimenting with ways to increase AI adoption for non-technical people. Basically, all companies are pushing for AI because it's all over the news and they feel left behind but most people have no clue where to start. I think 90% of people (ie non coders) are sufficiently well served by using cowork instead of claude code or something similar. If we can get people from sales, customer support, marketing, etc to collaborate with skills and cowork to form a company brain, I think it's gold. So I think there's opportunity for the community to share skills that work well for 1000s of…
Jun 2026 · claudinho.xyz
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Hello everyone, I am the creator of Stable Horde, a free, open sourced, crowdsourced, distributed cluster of Stable Diffusion (AKA AI image generation) workers. This service is provided for free and without any small print. The only thing one needs to be aware of is that your prompt goes to someone else's computer to be created and then sent back with all the caveats that entails. The main difference from all the other Stable Diffusion webuis and services are: * This service is free for everyone, without credits. While we do have a kudos system, this is only used for priority and you can…
2022 · stablehorde.net
- 18WB
Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
- 19AI
My focus has been shifting towards the ML alignment space recently, and in particular the ability to translate large transformer models into human understandable circuits and algorithms. This problem potentially isn't solvable, but it is one that some groups have had success with after large amounts of effort. In attempting to address this issue, I've been developing Transpector. A tool scaling up and reducing the barrier to entry of techniques that these teams have been showing success with. Techniques aiming to understand the internal mechanics of the model. Currently this tool is focused…
2023 · github.com
- 20IM
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
2024 · kitops.ml
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Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
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Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
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As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.
Jul 2026 · github.com
- 24IB
Hi HN, I’ve been tinkering with this for a while and finally have it in a decent state. It’s a plotter robot that draws caricatures from photos. I trained a diffusion model (Flux Kontext LoRA) on caricature images, 3D-printed a Sharpie mount for my Ender 3, and hacked together a pipeline that goes photo → caricature → G-code. After a lot of trial and error it’s working pretty well, and I put up a little site where you can try it out. Happy to answer questions or hear any feedback. Thanks!
2025 · caricature-bot.com
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