"Fiume O Morte " Brilliantly Dramatizes the Rise of a Demagogue
13 by rbanffy | 2 comments on Hacker News.
Wednesday, April 2, 2025
New top story on Hacker News: Show HN: A Chrome extension to give you back control over short-form videos
Show HN: A Chrome extension to give you back control over short-form videos
16 by darajava | 8 comments on Hacker News.
Hi HN! I built this little extension to prevent, in my opinion, the most offensive anti-pattern used by tech companies. That is removing the seek bar in short-form videos. The "seek bar" is the bar at the bottom of a video that progresses as you play the video, and that you can click on or drag to skip around. Why companies ever thought it was a good idea to get rid of this I don't know, but I find it infuriating, so I decided to add it back for myself and thought others might like it too. ReelControl adds a progress bar and seeking capabilities to videos on Instagram, YouTube Shorts, and Facebook Reels. I do sometimes enjoy watching short-form content and I've found that with this extension enabled I can be more mindful about it and get sucked in way less. I'm also on my phone less because I tend to favor the web versions of these platforms now. Open source--PRs and issues welcome! https://ift.tt/1Bsw4UR
16 by darajava | 8 comments on Hacker News.
Hi HN! I built this little extension to prevent, in my opinion, the most offensive anti-pattern used by tech companies. That is removing the seek bar in short-form videos. The "seek bar" is the bar at the bottom of a video that progresses as you play the video, and that you can click on or drag to skip around. Why companies ever thought it was a good idea to get rid of this I don't know, but I find it infuriating, so I decided to add it back for myself and thought others might like it too. ReelControl adds a progress bar and seeking capabilities to videos on Instagram, YouTube Shorts, and Facebook Reels. I do sometimes enjoy watching short-form content and I've found that with this extension enabled I can be more mindful about it and get sucked in way less. I'm also on my phone less because I tend to favor the web versions of these platforms now. Open source--PRs and issues welcome! https://ift.tt/1Bsw4UR
Tuesday, April 1, 2025
New top story on Hacker News: Show HN: Zig Topological Sort Library for Parallel Processing
Show HN: Zig Topological Sort Library for Parallel Processing
13 by ww520 | 1 comments on Hacker News.
I believe the best way to learn a language is by doing an in-depth project. This is my first Zig project intended for learning the ropes on publishing a Zig package. It turns out to be quite solid and performant. It might be a bit over-engineered. This little library is packed with the following features: - Building dependency graph from dependency data. - Performing topological sort on the dependency graph. - Generating dependence-free subsets for parallel processing. - Cycle detection and cycle reporting.
13 by ww520 | 1 comments on Hacker News.
I believe the best way to learn a language is by doing an in-depth project. This is my first Zig project intended for learning the ropes on publishing a Zig package. It turns out to be quite solid and performant. It might be a bit over-engineered. This little library is packed with the following features: - Building dependency graph from dependency data. - Performing topological sort on the dependency graph. - Generating dependence-free subsets for parallel processing. - Cycle detection and cycle reporting.
Monday, March 31, 2025
New top story on Hacker News: Show HN: GuMCP – Open-source MCP servers, hosted for free
Show HN: GuMCP – Open-source MCP servers, hosted for free
22 by murb | 3 comments on Hacker News.
Hello! We open sourced all our current MCP servers to platforms like Slack, Google sheets, Linear, Perplexity and will be contributing a few more integrations every day to the project. problems we're hoping to solve: - Many people are creating MCP servers for the same apps. They're scattered across different repos but flavors of the same thing. We're making one standardized mono project for all MCP servers. - Startups are charging for hosting MCP servers. This is blocking tons of people from being able to play around with MCP casually. We're hosting them for free. - Non-technical people should be able to use MCP without needing to learn how to clone a repo and set up a venv. We're trying to enable a one click integration if people want to use the free hosted service. The plan is to keep contributing until we have an MCP server for basically every useful app anyone could want.
22 by murb | 3 comments on Hacker News.
Hello! We open sourced all our current MCP servers to platforms like Slack, Google sheets, Linear, Perplexity and will be contributing a few more integrations every day to the project. problems we're hoping to solve: - Many people are creating MCP servers for the same apps. They're scattered across different repos but flavors of the same thing. We're making one standardized mono project for all MCP servers. - Startups are charging for hosting MCP servers. This is blocking tons of people from being able to play around with MCP casually. We're hosting them for free. - Non-technical people should be able to use MCP without needing to learn how to clone a repo and set up a venv. We're trying to enable a one click integration if people want to use the free hosted service. The plan is to keep contributing until we have an MCP server for basically every useful app anyone could want.
New top story on Hacker News: Launch HN: Augento (YC W25) – Fine-tune your agents with reinforcement learning
Launch HN: Augento (YC W25) – Fine-tune your agents with reinforcement learning
18 by lmeierhoefer | 1 comments on Hacker News.
Hi HN, we’re the cofounders of Augento ( https://augento.ai/ ). We’re building Deepseek R1-like fine-tuning as a service. You connect your agent, tell us when it’s right or wrong, and we deliver an LLM optimized for that agent. There’s a demo video https://www.youtube.com/watch?v=j5RQaTdRrKE , and our docs are at https://ift.tt/xvKsJUf . It’s open for anyone to use at https://augento.ai . Agents fail all the time, especially when you try to use them for something actually useful. Current solution approaches suck: prompting has intrinsic limits and supervised fine-tuning requires big explicit datasets that are hard to collect. Two months ago, the DeepSeek R1 paper outlined a way to post-train LLMs with (almost) pure reinforcement learning. We took up their research and built a fine-tuning platform around that. You let us intercept your agent's data flow, and we deliver you a fine-tuned open-source model, that is trained on the agent's specific task. Instead of providing big datasets of explicit fine-tuning samples, you provide a reward function, judging the model's outputs. Here are examples of what this can be used for: Coding Agent: We fine-tuned a coding agent that was constantly making syntax errors and failed to handle semantic edge cases properly. By providing a reward function that evaluated code against the compiler, the agent learned not to produce these errors. The fine-tuned model reduced critical bugs by 40% with just 20 training samples. MCP Tool Specialization: Imagine you have a custom set of internal tools using the MCP protocol, but your agent keeps selecting the wrong tool or passing incompatible parameters. You could fine-tune with a reward function that scores tool selection and parameter matching. Browser Agent Navigation: If you're building a browser agent that struggles with complex web UIs or specific sites, you could fine-tune it to better understand UI elements and navigation patterns. With a reward function that scores successful task completion (like "find the best price for this product" or "complete this multi-step form"), you could train an agent that better identifies clickable elements, understands form validation errors, and navigates through complex SPAs without getting stuck. VLA Robot Control: If you're using vision-language models to control robotic arms or other hardware, you could fine-tune for your specific actuator setup. With a reward function based on high-level task completion, you could train a Vision-Langauge-Action (VLA) model that translates natural language commands like "move the red block behind the blue cylinder" into actuator controls for your specific hardware. As you see from these examples, the current paradigm is best suited for "verifiable domains”, where it is possible to give an explicit function judging the model’s outputs. However, up next, we will also support an "alignment mode", where you don't have to provide a reward function but provide high-level feedback on past failure runs of your agent. Just tag where things went wrong, and we'll handle the rest. This makes it even easier to improve your agents without needing to write formal reward functions. Our platform is not itself open source, but it fine-tunes open-source language models. I.e. it is an alternative to the reinforcement fine-tuning API from OpenAI, but with Qwen, LLama, Deepseek, etc., and more customizability on the reward model. We charge users for the training and for their inference/interaction with the model later on ($0 monthly flat fee + training cost + inference cost). The platform is self-serving and open to use at https://ift.tt/WkDh6xd . We’ll give you $20 in training credits, which should be enough for connecting your agent and delivering some observable improvement on your use case. We’d love to hear your thoughts and feedback!
18 by lmeierhoefer | 1 comments on Hacker News.
Hi HN, we’re the cofounders of Augento ( https://augento.ai/ ). We’re building Deepseek R1-like fine-tuning as a service. You connect your agent, tell us when it’s right or wrong, and we deliver an LLM optimized for that agent. There’s a demo video https://www.youtube.com/watch?v=j5RQaTdRrKE , and our docs are at https://ift.tt/xvKsJUf . It’s open for anyone to use at https://augento.ai . Agents fail all the time, especially when you try to use them for something actually useful. Current solution approaches suck: prompting has intrinsic limits and supervised fine-tuning requires big explicit datasets that are hard to collect. Two months ago, the DeepSeek R1 paper outlined a way to post-train LLMs with (almost) pure reinforcement learning. We took up their research and built a fine-tuning platform around that. You let us intercept your agent's data flow, and we deliver you a fine-tuned open-source model, that is trained on the agent's specific task. Instead of providing big datasets of explicit fine-tuning samples, you provide a reward function, judging the model's outputs. Here are examples of what this can be used for: Coding Agent: We fine-tuned a coding agent that was constantly making syntax errors and failed to handle semantic edge cases properly. By providing a reward function that evaluated code against the compiler, the agent learned not to produce these errors. The fine-tuned model reduced critical bugs by 40% with just 20 training samples. MCP Tool Specialization: Imagine you have a custom set of internal tools using the MCP protocol, but your agent keeps selecting the wrong tool or passing incompatible parameters. You could fine-tune with a reward function that scores tool selection and parameter matching. Browser Agent Navigation: If you're building a browser agent that struggles with complex web UIs or specific sites, you could fine-tune it to better understand UI elements and navigation patterns. With a reward function that scores successful task completion (like "find the best price for this product" or "complete this multi-step form"), you could train an agent that better identifies clickable elements, understands form validation errors, and navigates through complex SPAs without getting stuck. VLA Robot Control: If you're using vision-language models to control robotic arms or other hardware, you could fine-tune for your specific actuator setup. With a reward function based on high-level task completion, you could train a Vision-Langauge-Action (VLA) model that translates natural language commands like "move the red block behind the blue cylinder" into actuator controls for your specific hardware. As you see from these examples, the current paradigm is best suited for "verifiable domains”, where it is possible to give an explicit function judging the model’s outputs. However, up next, we will also support an "alignment mode", where you don't have to provide a reward function but provide high-level feedback on past failure runs of your agent. Just tag where things went wrong, and we'll handle the rest. This makes it even easier to improve your agents without needing to write formal reward functions. Our platform is not itself open source, but it fine-tunes open-source language models. I.e. it is an alternative to the reinforcement fine-tuning API from OpenAI, but with Qwen, LLama, Deepseek, etc., and more customizability on the reward model. We charge users for the training and for their inference/interaction with the model later on ($0 monthly flat fee + training cost + inference cost). The platform is self-serving and open to use at https://ift.tt/WkDh6xd . We’ll give you $20 in training credits, which should be enough for connecting your agent and delivering some observable improvement on your use case. We’d love to hear your thoughts and feedback!