The best Hacker News stories from Show from the past day
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Show HN: A Digital Twin of my coffee roaster that runs in the browser
I built this website to host a data-driven model of my coffee sample roaster.<p>I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!<p>The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.<p>The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.<p>My next steps are to add other roasters and the ability to practice control/reference tracking.
Show HN: A Digital Twin of my coffee roaster that runs in the browser
I built this website to host a data-driven model of my coffee sample roaster.<p>I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!<p>The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.<p>The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.<p>My next steps are to add other roasters and the ability to practice control/reference tracking.
Show HN: A Digital Twin of my coffee roaster that runs in the browser
I built this website to host a data-driven model of my coffee sample roaster.<p>I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!<p>The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.<p>The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.<p>My next steps are to add other roasters and the ability to practice control/reference tracking.
Show HN: A Digital Twin of my coffee roaster that runs in the browser
I built this website to host a data-driven model of my coffee sample roaster.<p>I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!<p>The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.<p>The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.<p>My next steps are to add other roasters and the ability to practice control/reference tracking.
Show HN: Semantic search over the National Gallery of Art
Show HN: Semantic search over the National Gallery of Art
Show HN: Semantic search over the National Gallery of Art
Show HN: Open source, logical multi-master PostgreSQL replication
Show HN: Open source, logical multi-master PostgreSQL replication
Show HN: Open source, logical multi-master PostgreSQL replication
Show HN: I invented a new generative model and got accepted to ICLR
I invented Discrete Distribution Networks, a novel generative model with simple principles and unique properties, and the paper has been accepted to ICLR2025!<p>Modeling data distribution is challenging; DDN adopts a simple yet fundamentally different approach compared to mainstream generative models (Diffusion, GAN, VAE, autoregressive model):<p>1. The model generates multiple outputs simultaneously in a single forward pass, rather than just one output.
2. It uses these multiple outputs to approximate the target distribution of the training data.
3. These outputs together represent a discrete distribution. This is why we named it "Discrete Distribution Networks".<p>Every generative model has its unique properties, and DDN is no exception. Here, we highlight three characteristics of DDN:<p>- Zero-Shot Conditional Generation (ZSCG).
- One-dimensional discrete latent representation organized in a tree structure.
- Fully end-to-end differentiable.<p>Reviews from ICLR:<p>> I find the method novel and elegant. The novelty is very strong, and this should not be overlooked. This is a whole new method, very different from any of the existing generative models.
> This is a very good paper that can open a door to new directions in generative modeling.
Show HN: I invented a new generative model and got accepted to ICLR
I invented Discrete Distribution Networks, a novel generative model with simple principles and unique properties, and the paper has been accepted to ICLR2025!<p>Modeling data distribution is challenging; DDN adopts a simple yet fundamentally different approach compared to mainstream generative models (Diffusion, GAN, VAE, autoregressive model):<p>1. The model generates multiple outputs simultaneously in a single forward pass, rather than just one output.
2. It uses these multiple outputs to approximate the target distribution of the training data.
3. These outputs together represent a discrete distribution. This is why we named it "Discrete Distribution Networks".<p>Every generative model has its unique properties, and DDN is no exception. Here, we highlight three characteristics of DDN:<p>- Zero-Shot Conditional Generation (ZSCG).
- One-dimensional discrete latent representation organized in a tree structure.
- Fully end-to-end differentiable.<p>Reviews from ICLR:<p>> I find the method novel and elegant. The novelty is very strong, and this should not be overlooked. This is a whole new method, very different from any of the existing generative models.
> This is a very good paper that can open a door to new directions in generative modeling.
Show HN: I invented a new generative model and got accepted to ICLR
I invented Discrete Distribution Networks, a novel generative model with simple principles and unique properties, and the paper has been accepted to ICLR2025!<p>Modeling data distribution is challenging; DDN adopts a simple yet fundamentally different approach compared to mainstream generative models (Diffusion, GAN, VAE, autoregressive model):<p>1. The model generates multiple outputs simultaneously in a single forward pass, rather than just one output.
2. It uses these multiple outputs to approximate the target distribution of the training data.
3. These outputs together represent a discrete distribution. This is why we named it "Discrete Distribution Networks".<p>Every generative model has its unique properties, and DDN is no exception. Here, we highlight three characteristics of DDN:<p>- Zero-Shot Conditional Generation (ZSCG).
- One-dimensional discrete latent representation organized in a tree structure.
- Fully end-to-end differentiable.<p>Reviews from ICLR:<p>> I find the method novel and elegant. The novelty is very strong, and this should not be overlooked. This is a whole new method, very different from any of the existing generative models.
> This is a very good paper that can open a door to new directions in generative modeling.
Show HN: I wrote a full text search engine in Go
Show HN: I wrote a full text search engine in Go
Show HN: I wrote a full text search engine in Go
Show HN: I wrote a full text search engine in Go
Show HN: I've built a tiny hand-held keyboard
I bet you didn't knew you can use modelling clay (as opposed to 3d printing) to make nice devices by hand :)
Show HN: I've built a tiny hand-held keyboard
I bet you didn't knew you can use modelling clay (as opposed to 3d printing) to make nice devices by hand :)
Show HN: I've built a tiny hand-held keyboard
I bet you didn't knew you can use modelling clay (as opposed to 3d printing) to make nice devices by hand :)