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Show HN: Cardo ‒ Open Source desktop podcast client

Show HN: Cardo - Open Source Desktop Podcast Client<p>Hi, I'm an amateur developer from Spain. I have released this desktop podcast client that works on Windows, Mac and Linux.<p>It's a modest project, but it might be useful for you, it has syncing capabilities with Antennapod, Kasts, Repod or other clients. You can manage your subscriptions, queue episodes and even download them to listen to later.<p>I look forward to hearing your thoughts.

Show HN: Cardo ‒ Open Source desktop podcast client

Show HN: Cardo - Open Source Desktop Podcast Client<p>Hi, I'm an amateur developer from Spain. I have released this desktop podcast client that works on Windows, Mac and Linux.<p>It's a modest project, but it might be useful for you, it has syncing capabilities with Antennapod, Kasts, Repod or other clients. You can manage your subscriptions, queue episodes and even download them to listen to later.<p>I look forward to hearing your thoughts.

Show HN: We open-sourced our compost monitoring tech

I'm from a compost tech startup (Monty Compost Co.) focused on making composting more efficient for households and industrial facilities. But our tech isn’t just for composting— it’s a versatile system that can be repurposed for a wide range of applications. So, we’ve made it open source for anyone to experiment with!<p>One of the exciting things about our open-source compost monitoring tech is its flexibility. You can connect it to platforms like Raspberry Pi, Arduino, or other single-board computers to expand its capabilities or integrate it into your own projects.<p>Our system includes sensors for: * Gas composition * Temperature * Moisture levels * Air pressure<p>All data can be exported as CSV files for analysis. While it’s originally built for monitoring compost, the hardware and data capabilities are versatile and could be repurposed for other applications (IoT, environmental monitoring, etc.)<p>Hacker’s Guide to Monty Tech: <a href="https://github.com/gtls64/MontyHome-Hackers-Guide">https://github.com/gtls64/MontyHome-Hackers-Guide</a><p>If you’re into data, sensors, or creative tech hacks, we’d love for you to check it out and let us know what you build!

Show HN: We open-sourced our compost monitoring tech

I'm from a compost tech startup (Monty Compost Co.) focused on making composting more efficient for households and industrial facilities. But our tech isn’t just for composting— it’s a versatile system that can be repurposed for a wide range of applications. So, we’ve made it open source for anyone to experiment with!<p>One of the exciting things about our open-source compost monitoring tech is its flexibility. You can connect it to platforms like Raspberry Pi, Arduino, or other single-board computers to expand its capabilities or integrate it into your own projects.<p>Our system includes sensors for: * Gas composition * Temperature * Moisture levels * Air pressure<p>All data can be exported as CSV files for analysis. While it’s originally built for monitoring compost, the hardware and data capabilities are versatile and could be repurposed for other applications (IoT, environmental monitoring, etc.)<p>Hacker’s Guide to Monty Tech: <a href="https://github.com/gtls64/MontyHome-Hackers-Guide">https://github.com/gtls64/MontyHome-Hackers-Guide</a><p>If you’re into data, sensors, or creative tech hacks, we’d love for you to check it out and let us know what you build!

Show HN: We open-sourced our compost monitoring tech

I'm from a compost tech startup (Monty Compost Co.) focused on making composting more efficient for households and industrial facilities. But our tech isn’t just for composting— it’s a versatile system that can be repurposed for a wide range of applications. So, we’ve made it open source for anyone to experiment with!<p>One of the exciting things about our open-source compost monitoring tech is its flexibility. You can connect it to platforms like Raspberry Pi, Arduino, or other single-board computers to expand its capabilities or integrate it into your own projects.<p>Our system includes sensors for: * Gas composition * Temperature * Moisture levels * Air pressure<p>All data can be exported as CSV files for analysis. While it’s originally built for monitoring compost, the hardware and data capabilities are versatile and could be repurposed for other applications (IoT, environmental monitoring, etc.)<p>Hacker’s Guide to Monty Tech: <a href="https://github.com/gtls64/MontyHome-Hackers-Guide">https://github.com/gtls64/MontyHome-Hackers-Guide</a><p>If you’re into data, sensors, or creative tech hacks, we’d love for you to check it out and let us know what you build!

Show HN: Bike route planner that follows almost only official bike trails

Hey guys, I built a route planner that is mostly focused on bike touring and using existing bike infrastructure.<p>For each request you're shown what bike tracks/trails your route uses and can further explore them by showing them on map or going to the official trail route.<p>The main idea for the app is to have a friendly and easy to use planner that would make heavy use of official bike trails data (mainly from OpenStreetMap) and make it easy to plan a longer trip using the best possible bike routes out there.<p>Currently the app only works for the Euro region but I'm planning to add North America very soon and then rest of the world.<p>Technical overview: Route finding - Graphhopper sitting in a docker container on a Hetzner server somewhere in Germany. It has 38 GB of graph data(Europe) loaded into RAM for a fast graph traversal.<p>Web App - Next.js 14 with Typescript, backend on the newest version of .NET<p>Map tiles - right now I'm using MapTiler their free tier but planning to switch to my own home server soon and host the maps on it.

Show HN: Bike route planner that follows almost only official bike trails

Hey guys, I built a route planner that is mostly focused on bike touring and using existing bike infrastructure.<p>For each request you're shown what bike tracks/trails your route uses and can further explore them by showing them on map or going to the official trail route.<p>The main idea for the app is to have a friendly and easy to use planner that would make heavy use of official bike trails data (mainly from OpenStreetMap) and make it easy to plan a longer trip using the best possible bike routes out there.<p>Currently the app only works for the Euro region but I'm planning to add North America very soon and then rest of the world.<p>Technical overview: Route finding - Graphhopper sitting in a docker container on a Hetzner server somewhere in Germany. It has 38 GB of graph data(Europe) loaded into RAM for a fast graph traversal.<p>Web App - Next.js 14 with Typescript, backend on the newest version of .NET<p>Map tiles - right now I'm using MapTiler their free tier but planning to switch to my own home server soon and host the maps on it.

Show HN: Bike route planner that follows almost only official bike trails

Hey guys, I built a route planner that is mostly focused on bike touring and using existing bike infrastructure.<p>For each request you're shown what bike tracks/trails your route uses and can further explore them by showing them on map or going to the official trail route.<p>The main idea for the app is to have a friendly and easy to use planner that would make heavy use of official bike trails data (mainly from OpenStreetMap) and make it easy to plan a longer trip using the best possible bike routes out there.<p>Currently the app only works for the Euro region but I'm planning to add North America very soon and then rest of the world.<p>Technical overview: Route finding - Graphhopper sitting in a docker container on a Hetzner server somewhere in Germany. It has 38 GB of graph data(Europe) loaded into RAM for a fast graph traversal.<p>Web App - Next.js 14 with Typescript, backend on the newest version of .NET<p>Map tiles - right now I'm using MapTiler their free tier but planning to switch to my own home server soon and host the maps on it.

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Llama 3.2 Interpretability with Sparse Autoencoders

I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3].<p>I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback.<p>Cheers<p>[1] <a href="https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" rel="nofollow">https://transformer-circuits.pub/2024/scaling-monosemanticit...</a><p>[2] <a href="https://arxiv.org/abs/2406.04093" rel="nofollow">https://arxiv.org/abs/2406.04093</a><p>[3] <a href="https://arxiv.org/abs/2408.05147" rel="nofollow">https://arxiv.org/abs/2408.05147</a>

Show HN: Self-Host Next.js in Production

Show HN: Self-Host Next.js in Production

Show HN: Rust library for numerical integration of real-valued functions

Integrate is a fast, small, lightweight Rust library for performing numerical integration of real-valued functions. It is designed to integrate functions, providing a simple and efficient way to approximate definite integrals using various numerical methods.<p>Integrate supports a variety of numerical integration techniques: - Newton-Cotes methods:<p><pre><code> - Rectangle Rule. - Trapezoidal Rule. - Simpson's Rule. - Newton's 3/8 Rule. </code></pre> - Gauss quadrature methods:<p><pre><code> - Gauss-Legendre. - Gauss-Laguerre. - Gauss-Hermite. - Gauss-Chebyshev First Kind. - Gauss-Chebyshev Second Kind. </code></pre> - Adaptive Methods:<p><pre><code> - Adaptive Simpson's method </code></pre> - Romberg’s method.

Show HN: Rust library for numerical integration of real-valued functions

Integrate is a fast, small, lightweight Rust library for performing numerical integration of real-valued functions. It is designed to integrate functions, providing a simple and efficient way to approximate definite integrals using various numerical methods.<p>Integrate supports a variety of numerical integration techniques: - Newton-Cotes methods:<p><pre><code> - Rectangle Rule. - Trapezoidal Rule. - Simpson's Rule. - Newton's 3/8 Rule. </code></pre> - Gauss quadrature methods:<p><pre><code> - Gauss-Legendre. - Gauss-Laguerre. - Gauss-Hermite. - Gauss-Chebyshev First Kind. - Gauss-Chebyshev Second Kind. </code></pre> - Adaptive Methods:<p><pre><code> - Adaptive Simpson's method </code></pre> - Romberg’s method.

Show HN: Autotab – Programmable AI browser for turning web tasks into APIs

Hey HN, we're Alexi and Jonas the co-founders of Autotab (<a href="https://autotab.com">https://autotab.com</a>). Autotab is a chrome-based browser you can teach to do complex tasks, with a simple API for running them from your app or backend.<p>Here is a walkthrough of how it works: <a href="https://youtu.be/63co74JHy1k" rel="nofollow">https://youtu.be/63co74JHy1k</a>, and you can try it for free at <a href="https://autotab.com">https://autotab.com</a> by downloading the app.<p>Why a dedicated editor?<p>The number one blocker we've found in building more flexible, agentic automations is performance quality BY FAR (<a href="https://www.langchain.com/stateofaiagents#barriers-and-challenges" rel="nofollow">https://www.langchain.com/stateofaiagents#barriers-and-chall...</a>). For all the talk of cost, latency, and safety, the fact is most people are still just struggling to get agents to work. The keys to solving reliability are better models, yes, but also intent specification. Even humans don't zero-shot these tasks from a prompt. They need to be shown how to perform them, and then refined with question-asking + feedback over time. It is also quite difficult to formulate complete requirements on the spot from memory.<p>The editor makes it easy to build the specification up as you step through your workflow, while generating successful task trajectories for the model. This is the only way we've been able to get the reliability we need for production use cases.<p>But why build a browser?<p>Autotab started as a Chrome extension (with a Show HN post! <a href="https://news.ycombinator.com/item?id=37943931">https://news.ycombinator.com/item?id=37943931</a>). As we iterated with users, we realized that we needed to focus on creating the control surface for intent specification, and that being stuck in a chrome sidepanel wasn't going to work. We also knew that we needed a level of control for the model that we couldn't get without owning the browser. In Autotab, the browser becomes a canvas on which the user and the model are taking turns showing and explaining the task.<p>Key features:<p>1. Self-healing automations that don't break when sites change<p>2. Dedicated authoring tool that builds memory for the model while defining steps for the automation<p>3. Control flows and deep configurability to keep automations on track, even when navigating complex reasoning tasks<p>4. Works with any website (no site-specific APIs needed)<p>5. Runs securely in the cloud or locally<p>6. Simple REST API + client libraries for Python, Node<p>We'd love to get any early feedback from the HN community, ideas for where you'd like the product to go, or experiences in this space. We will be in the comments for the next few hours to respond!

Show HN: Autotab – Programmable AI browser for turning web tasks into APIs

Hey HN, we're Alexi and Jonas the co-founders of Autotab (<a href="https://autotab.com">https://autotab.com</a>). Autotab is a chrome-based browser you can teach to do complex tasks, with a simple API for running them from your app or backend.<p>Here is a walkthrough of how it works: <a href="https://youtu.be/63co74JHy1k" rel="nofollow">https://youtu.be/63co74JHy1k</a>, and you can try it for free at <a href="https://autotab.com">https://autotab.com</a> by downloading the app.<p>Why a dedicated editor?<p>The number one blocker we've found in building more flexible, agentic automations is performance quality BY FAR (<a href="https://www.langchain.com/stateofaiagents#barriers-and-challenges" rel="nofollow">https://www.langchain.com/stateofaiagents#barriers-and-chall...</a>). For all the talk of cost, latency, and safety, the fact is most people are still just struggling to get agents to work. The keys to solving reliability are better models, yes, but also intent specification. Even humans don't zero-shot these tasks from a prompt. They need to be shown how to perform them, and then refined with question-asking + feedback over time. It is also quite difficult to formulate complete requirements on the spot from memory.<p>The editor makes it easy to build the specification up as you step through your workflow, while generating successful task trajectories for the model. This is the only way we've been able to get the reliability we need for production use cases.<p>But why build a browser?<p>Autotab started as a Chrome extension (with a Show HN post! <a href="https://news.ycombinator.com/item?id=37943931">https://news.ycombinator.com/item?id=37943931</a>). As we iterated with users, we realized that we needed to focus on creating the control surface for intent specification, and that being stuck in a chrome sidepanel wasn't going to work. We also knew that we needed a level of control for the model that we couldn't get without owning the browser. In Autotab, the browser becomes a canvas on which the user and the model are taking turns showing and explaining the task.<p>Key features:<p>1. Self-healing automations that don't break when sites change<p>2. Dedicated authoring tool that builds memory for the model while defining steps for the automation<p>3. Control flows and deep configurability to keep automations on track, even when navigating complex reasoning tasks<p>4. Works with any website (no site-specific APIs needed)<p>5. Runs securely in the cloud or locally<p>6. Simple REST API + client libraries for Python, Node<p>We'd love to get any early feedback from the HN community, ideas for where you'd like the product to go, or experiences in this space. We will be in the comments for the next few hours to respond!