How a $2k 'Made in the USA' Phone Is Manufactured
32 by jaredwiener | 12 comments on Hacker News.
Thursday, April 10, 2025
Wednesday, April 9, 2025
Tuesday, April 8, 2025
New top story on Hacker News: Ask HN: Do you still use search engines?
Ask HN: Do you still use search engines?
43 by davidkuennen | 123 comments on Hacker News.
Today, I noticed that my behavior has shifted over the past few months. Right now, I exclusively use ChatGPT for any kind of search or question. Using Google now feels completely lackluster in comparison. I've noticed the same thing happening in my circle of friends as well—and they don’t even have a technical background. How about you?
43 by davidkuennen | 123 comments on Hacker News.
Today, I noticed that my behavior has shifted over the past few months. Right now, I exclusively use ChatGPT for any kind of search or question. Using Google now feels completely lackluster in comparison. I've noticed the same thing happening in my circle of friends as well—and they don’t even have a technical background. How about you?
Monday, April 7, 2025
Sunday, April 6, 2025
Saturday, April 5, 2025
Friday, April 4, 2025
Thursday, April 3, 2025
Wednesday, April 2, 2025
Tuesday, April 1, 2025
New top story on Hacker News: Show HN: Make SVGs interactive in React with 1 line
Show HN: Make SVGs interactive in React with 1 line
6 by shantingHou | 1 comments on Hacker News.
Hey HN I built svggles (npm: interactive-illustrations), a React utility that makes it easy to add playful, interactive SVGs to your frontend. It supports mouse-tracking, scroll, hover, and other common interactions, and it's designed to be lightweight and intuitive for React devs. The inspiration came from my time playing with p5.js — I loved how expressive and fun it was to create interactive visuals. But I also wanted to bring that kind of creative freedom to everyday frontend work, in a way that fits naturally into the React ecosystem. My goal is to help frontend developers make their UIs feel more alive — not just functional, but fun. I also know creativity thrives in community, so it's open source and I’d love to see contributions from artists, developers, or anyone interested in visual interaction. Links: Website + Docs: svggles.vercel.app GitHub: github.com/shantinghou/interactive-illustrations NPM: interactive-illustrations Let me know what you think — ideas, feedback, and contributions are all welcome
6 by shantingHou | 1 comments on Hacker News.
Hey HN I built svggles (npm: interactive-illustrations), a React utility that makes it easy to add playful, interactive SVGs to your frontend. It supports mouse-tracking, scroll, hover, and other common interactions, and it's designed to be lightweight and intuitive for React devs. The inspiration came from my time playing with p5.js — I loved how expressive and fun it was to create interactive visuals. But I also wanted to bring that kind of creative freedom to everyday frontend work, in a way that fits naturally into the React ecosystem. My goal is to help frontend developers make their UIs feel more alive — not just functional, but fun. I also know creativity thrives in community, so it's open source and I’d love to see contributions from artists, developers, or anyone interested in visual interaction. Links: Website + Docs: svggles.vercel.app GitHub: github.com/shantinghou/interactive-illustrations NPM: interactive-illustrations Let me know what you think — ideas, feedback, and contributions are all welcome
New top story on Hacker News: Show HN: Qwen-2.5-32B is now the best open source OCR model
Show HN: Qwen-2.5-32B is now the best open source OCR model
13 by themanmaran | 2 comments on Hacker News.
Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3 (27B) only scored 42.9%. Particularly surprising given that it's architecture is based on Gemini 2.0 which still tops the accuracy chart. The data set and benchmark runner is fully open source. You can check out the code and reproduction steps here: - https://ift.tt/nyvREUo... - https://ift.tt/WLwDHGS - https://ift.tt/nm1bhrJ
13 by themanmaran | 2 comments on Hacker News.
Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3 (27B) only scored 42.9%. Particularly surprising given that it's architecture is based on Gemini 2.0 which still tops the accuracy chart. The data set and benchmark runner is fully open source. You can check out the code and reproduction steps here: - https://ift.tt/nyvREUo... - https://ift.tt/WLwDHGS - https://ift.tt/nm1bhrJ
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
18 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.
18 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.
Sunday, March 30, 2025
Saturday, March 29, 2025
Friday, March 28, 2025
Thursday, March 27, 2025
Wednesday, March 26, 2025
Tuesday, March 25, 2025
Monday, March 24, 2025
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Saturday, March 22, 2025
Friday, March 21, 2025
Thursday, March 20, 2025
New top story on Hacker News: Show HN: AgentKit – JavaScript Alternative to OpenAI Agents SDK with Native MCP
Show HN: AgentKit – JavaScript Alternative to OpenAI Agents SDK with Native MCP
35 by tonyhb | 9 comments on Hacker News.
Hi HN! I’m Tony, co-founder of Inngest. I wanted to share AgentKit, our Typescript multi-agent library we’ve been cooking and testing with some early users in prod for months. Although OpenAI’s Agents SDK has been launched since, we think an Agent framework should offer more deterministic and flexible routing, work with multiple model providers, embrace MCP (for rich tooling), and support the unstoppable and growing community of TypeScript AI developers by enabling a smooth transition to production use cases. This is why we are building AgentKit, and we’re really excited about it for a few reasons: Firstly, it’s simple. We embrace KISS principles brought by Anthropic and HuggingFace by allowing you to gradually add autonomy to your AgentKit program using primitives: - Agents: LLM calls that can be combined with prompts, tools, and MCP native support. - Networks: a simple way to get Agents to collaborate with a shared State, including handoff. - State: combines conversation history with a fully typed state machine, used in routing. - Routers: where the autonomy lives, from code-based to LLM-based (ex: ReAct) orchestration The routers are where the magic happens, and allow you to build deterministic, reliable, testable agents. AgentKit routing works as follows: the network calls itself in a loop, inspecting the State to determine which agents to call next using a router. The returned agent runs, then optionally updates state data using its tools. On the next loop, the network inspects state data and conversation history, and determines which new agent to run. This fully typed state machine routing allows you to deterministically build agents using any of the effective agent patterns — which means your code is easy to read, edit, understand, and debug. This also makes handoff incredibly easy: you define when agents should hand off to each other using regular code and state (or by calling an LLM in the router for AI-based routing). This is similar to the OpenAI Agents SDK but easier to manage, plan, and build. Then comes the local development and moving to production capabilities. AgentKit is compatible with Inngest’s tooling, meaning that you can test agents using Inngest’s local DevServer, which provides traces, inputs, outputs, replay, tool, and MCP inputs and outputs, and (soon) a step-over debugger so that you can easily understand and visually see what's happening in the agent loop. In production, you can also optionally combine AgentKit with Inngest for fault-tolerant execution. Each agent’s LLM call is wrapped in a step, and tools can use multiple steps to incorporate things like human-in-the-loop. This gives you native orchestration, observability, and out-of-the-box scale. You will find the documentation as an example of an AgentKit SWE-bench and multiple Coding Agent examples. It’s fully open-source under the Apache 2 license. If you want to get started: - npm: npm i @inngest/agent-kit - GitHub: https://ift.tt/SsMORIL - Docs: https://ift.tt/5Qs6BTo We’re excited to finally launch AgentKit; let us know what you think!
35 by tonyhb | 9 comments on Hacker News.
Hi HN! I’m Tony, co-founder of Inngest. I wanted to share AgentKit, our Typescript multi-agent library we’ve been cooking and testing with some early users in prod for months. Although OpenAI’s Agents SDK has been launched since, we think an Agent framework should offer more deterministic and flexible routing, work with multiple model providers, embrace MCP (for rich tooling), and support the unstoppable and growing community of TypeScript AI developers by enabling a smooth transition to production use cases. This is why we are building AgentKit, and we’re really excited about it for a few reasons: Firstly, it’s simple. We embrace KISS principles brought by Anthropic and HuggingFace by allowing you to gradually add autonomy to your AgentKit program using primitives: - Agents: LLM calls that can be combined with prompts, tools, and MCP native support. - Networks: a simple way to get Agents to collaborate with a shared State, including handoff. - State: combines conversation history with a fully typed state machine, used in routing. - Routers: where the autonomy lives, from code-based to LLM-based (ex: ReAct) orchestration The routers are where the magic happens, and allow you to build deterministic, reliable, testable agents. AgentKit routing works as follows: the network calls itself in a loop, inspecting the State to determine which agents to call next using a router. The returned agent runs, then optionally updates state data using its tools. On the next loop, the network inspects state data and conversation history, and determines which new agent to run. This fully typed state machine routing allows you to deterministically build agents using any of the effective agent patterns — which means your code is easy to read, edit, understand, and debug. This also makes handoff incredibly easy: you define when agents should hand off to each other using regular code and state (or by calling an LLM in the router for AI-based routing). This is similar to the OpenAI Agents SDK but easier to manage, plan, and build. Then comes the local development and moving to production capabilities. AgentKit is compatible with Inngest’s tooling, meaning that you can test agents using Inngest’s local DevServer, which provides traces, inputs, outputs, replay, tool, and MCP inputs and outputs, and (soon) a step-over debugger so that you can easily understand and visually see what's happening in the agent loop. In production, you can also optionally combine AgentKit with Inngest for fault-tolerant execution. Each agent’s LLM call is wrapped in a step, and tools can use multiple steps to incorporate things like human-in-the-loop. This gives you native orchestration, observability, and out-of-the-box scale. You will find the documentation as an example of an AgentKit SWE-bench and multiple Coding Agent examples. It’s fully open-source under the Apache 2 license. If you want to get started: - npm: npm i @inngest/agent-kit - GitHub: https://ift.tt/SsMORIL - Docs: https://ift.tt/5Qs6BTo We’re excited to finally launch AgentKit; let us know what you think!
Wednesday, March 19, 2025
New top story on Hacker News: Show HN: We built an agentic image editor that preserves the original structure
Show HN: We built an agentic image editor that preserves the original structure
8 by sakofchit | 5 comments on Hacker News.
Hi everyone, I’ve been experimenting with app where you can edit images in your camera roll simply by tweaking your photo’s metadata (changing location/time) and our agent will contextually regenerate the photo in that place & time in one shot. There's no prompting involved. One of the hardest problems we’ve seen with these ai image editing/creation tools is that they struggle with preserving the subjects of the original image (faces, genders, number of people, bodies, animals, etc), and I think we’ve gotten a step closer to making it feel more realistic. The gallery has some examples that people have been regenerating. https://ift.tt/TQxIOb1 Here’s a demo: https://ift.tt/yXbx63Y Feel free to dm me on Twitter: https://twitter.com/sakofchit if you’d like to try out the TestFlight in the meantime Would love to know what y'all think!
8 by sakofchit | 5 comments on Hacker News.
Hi everyone, I’ve been experimenting with app where you can edit images in your camera roll simply by tweaking your photo’s metadata (changing location/time) and our agent will contextually regenerate the photo in that place & time in one shot. There's no prompting involved. One of the hardest problems we’ve seen with these ai image editing/creation tools is that they struggle with preserving the subjects of the original image (faces, genders, number of people, bodies, animals, etc), and I think we’ve gotten a step closer to making it feel more realistic. The gallery has some examples that people have been regenerating. https://ift.tt/TQxIOb1 Here’s a demo: https://ift.tt/yXbx63Y Feel free to dm me on Twitter: https://twitter.com/sakofchit if you’d like to try out the TestFlight in the meantime Would love to know what y'all think!
Tuesday, March 18, 2025
Monday, March 17, 2025
Sunday, March 16, 2025
New top story on Hacker News: Show HN: 10 teams are racing to build a pivotal tracker replacement
Show HN: 10 teams are racing to build a pivotal tracker replacement
3 by jFriedensreich | 0 comments on Hacker News.
A lot has changed since the shutdown of pivotal tracker was discussed here. As there were no viable alternatives it seems every month there was a new project popping up. With the last month before the sunsetting approaching, it starts to get exciting who will make it in time, who stays in the race and what the differentiating features of the projects will be.
3 by jFriedensreich | 0 comments on Hacker News.
A lot has changed since the shutdown of pivotal tracker was discussed here. As there were no viable alternatives it seems every month there was a new project popping up. With the last month before the sunsetting approaching, it starts to get exciting who will make it in time, who stays in the race and what the differentiating features of the projects will be.
Saturday, March 15, 2025
Friday, March 14, 2025
New top story on Hacker News: Show HN: Pi Labs – AI scoring and optimization tools for software engineers
Show HN: Pi Labs – AI scoring and optimization tools for software engineers
11 by achintms | 0 comments on Hacker News.
Hey HN, after years building some of the core AI and NLU systems in Google Search, we decided to leave and build outside. Our goal was to put the advanced ML and DS techniques we’ve been using in the hands of all software engineers, so that everyone can build AI and Search apps at the same level of performance and sophistication as the big labs. This was a hard technical challenge but we were very inspired by the MVC architecture for Web development. The intuition there was that when a data model changes, its view would get auto-updated. We built a similar architecture for AI. On one side is a scoring system, which encapsulates in a set of metrics what’s good about the AI application. On the other side is a set of optimizers that “compile” against this scorer - prompt optimization, data filtering, synthetic data generation, supervised learning, RL, etc. The scoring system can be calibrated using developer, user or rater feedback, and once it’s updated, all the optimizers get recompiled against it. The result is a setup that makes it easy to incrementally improve the quality of your AI in a tight feedback loop: You update your scorers, they auto-update your optimizers, your app gets better, you see that improvement in interpretable scores, and then you repeat, progressing from simpler to more advanced optimizers and from off-the-shelf to calibrated scorers. We would love your feedback on this approach. https://build.withpi.ai has a set of playgrounds to help you quickly build a scorer and multiple optimizers. No sign in required. https://code.withpi.ai has the API reference and Notebook links. Finally, we have a Loom demo [1]. More technical details Scorers: Our scoring system has three key differences from the common LLM-as-a-judge pattern. First, rather than a single label or metric from an LLM judge, our scoring system is represented as a tunable tree of metrics, with 20+ dimensions which get combined into a final (non-linear) weighted score. The tree structure makes scores easily interpretable (just look at the breakdown by dimension), extensible (just add/remove a dimension), and adjustable (just re-tune the weights). Training the scoring system with labeled/preference data adjusts the weights. You can automate this process with user feedback signals, resulting in a tight feedback loop. Second, our scoring system handles natural language dimensions (great for free-form, qualitative questions requiring NLU) alongside quantitative dimensions (like computations over dates or doc length, which can be provided in Python) in the same tree. When calibrating with your labeled or preference data, the scorer learns how to balance these. Third, for natural language scoring, we use specialized smaller encoder models rather than autoregressive models. Encoders are a natural fit for scoring as they are faster and cheaper to run, easier to fine-tune, and more suitable architecturally (bi-directional attention with regression or classification head) than similar sized decoder models. For example, we can score 20+ dimensions in sub-100ms, making it possible to use scoring everywhere from evaluation to agent orchestration to reward modeling. Optimizers: We took the most salient ML techniques and reformulated them as optimizers against our scoring system e.g. for DSPy, the scoring system acts as its validator. For GRPO, the scoring system acts as its reward model. We’re keen to hear the community’s feedback on which techniques to add next. Overall stack: Playgrounds next.js and Vercel. AI: Runpod and GCP for training GPUs, TRL for training algos, ModernBert & Llama as base models. GCP and Azure for 4o and Anthropic calls. We’d love your feedback and perspectives: Our team will be around to answer questions and discuss. If there’s a lot of interest, happy to host a live session! - Achint, co-founder of Pi Labs [1] https://ift.tt/TSn2KBs
11 by achintms | 0 comments on Hacker News.
Hey HN, after years building some of the core AI and NLU systems in Google Search, we decided to leave and build outside. Our goal was to put the advanced ML and DS techniques we’ve been using in the hands of all software engineers, so that everyone can build AI and Search apps at the same level of performance and sophistication as the big labs. This was a hard technical challenge but we were very inspired by the MVC architecture for Web development. The intuition there was that when a data model changes, its view would get auto-updated. We built a similar architecture for AI. On one side is a scoring system, which encapsulates in a set of metrics what’s good about the AI application. On the other side is a set of optimizers that “compile” against this scorer - prompt optimization, data filtering, synthetic data generation, supervised learning, RL, etc. The scoring system can be calibrated using developer, user or rater feedback, and once it’s updated, all the optimizers get recompiled against it. The result is a setup that makes it easy to incrementally improve the quality of your AI in a tight feedback loop: You update your scorers, they auto-update your optimizers, your app gets better, you see that improvement in interpretable scores, and then you repeat, progressing from simpler to more advanced optimizers and from off-the-shelf to calibrated scorers. We would love your feedback on this approach. https://build.withpi.ai has a set of playgrounds to help you quickly build a scorer and multiple optimizers. No sign in required. https://code.withpi.ai has the API reference and Notebook links. Finally, we have a Loom demo [1]. More technical details Scorers: Our scoring system has three key differences from the common LLM-as-a-judge pattern. First, rather than a single label or metric from an LLM judge, our scoring system is represented as a tunable tree of metrics, with 20+ dimensions which get combined into a final (non-linear) weighted score. The tree structure makes scores easily interpretable (just look at the breakdown by dimension), extensible (just add/remove a dimension), and adjustable (just re-tune the weights). Training the scoring system with labeled/preference data adjusts the weights. You can automate this process with user feedback signals, resulting in a tight feedback loop. Second, our scoring system handles natural language dimensions (great for free-form, qualitative questions requiring NLU) alongside quantitative dimensions (like computations over dates or doc length, which can be provided in Python) in the same tree. When calibrating with your labeled or preference data, the scorer learns how to balance these. Third, for natural language scoring, we use specialized smaller encoder models rather than autoregressive models. Encoders are a natural fit for scoring as they are faster and cheaper to run, easier to fine-tune, and more suitable architecturally (bi-directional attention with regression or classification head) than similar sized decoder models. For example, we can score 20+ dimensions in sub-100ms, making it possible to use scoring everywhere from evaluation to agent orchestration to reward modeling. Optimizers: We took the most salient ML techniques and reformulated them as optimizers against our scoring system e.g. for DSPy, the scoring system acts as its validator. For GRPO, the scoring system acts as its reward model. We’re keen to hear the community’s feedback on which techniques to add next. Overall stack: Playgrounds next.js and Vercel. AI: Runpod and GCP for training GPUs, TRL for training algos, ModernBert & Llama as base models. GCP and Azure for 4o and Anthropic calls. We’d love your feedback and perspectives: Our team will be around to answer questions and discuss. If there’s a lot of interest, happy to host a live session! - Achint, co-founder of Pi Labs [1] https://ift.tt/TSn2KBs
New top story on Hacker News: Show HN: OCR Benchmark Focusing on Automation
Show HN: OCR Benchmark Focusing on Automation
3 by prats226 | 0 comments on Hacker News.
OCR/Document extraction field has seen lot of action recently with releases like Mixtral OCR, Andrew Ng's agentic document processing etc. Also there are several benchmarks for OCR, however all testing for something slightly different which make good comparison of models very hard. To give an example, some models like mixtral-ocr only try to convert a document to markdown format. You have to use another LLM on top of it to get the final result. Some VLM’s directly give structured information like key fields from documents like invoices, but you have to either add business rules on top of it or use some LLM as a judge kind of system to get sense of which output needs to be manually reviewed or can be taken as correct output. No benchmark attempts to measure the actual rate of automation you can achieve. We have tried to solve this problem with a benchmark that is only applicable for documents/usecases where you are looking for automation and its trying to measure that end to end automation level of different models or systems. We have collected a dataset that represents documents like invoices etc which are applicable in processes where automation is needed vs are more copilot in nature where you would need to chat with document. Also have annotated these documents and published the dataset and repo so it can be extended. Here is writeup: https://ift.tt/qyvACdU Dataset: https://ift.tt/sEQR058 Github: https://ift.tt/o8Bk4F0 Looking for suggestions on how this benchmark can be improved further.
3 by prats226 | 0 comments on Hacker News.
OCR/Document extraction field has seen lot of action recently with releases like Mixtral OCR, Andrew Ng's agentic document processing etc. Also there are several benchmarks for OCR, however all testing for something slightly different which make good comparison of models very hard. To give an example, some models like mixtral-ocr only try to convert a document to markdown format. You have to use another LLM on top of it to get the final result. Some VLM’s directly give structured information like key fields from documents like invoices, but you have to either add business rules on top of it or use some LLM as a judge kind of system to get sense of which output needs to be manually reviewed or can be taken as correct output. No benchmark attempts to measure the actual rate of automation you can achieve. We have tried to solve this problem with a benchmark that is only applicable for documents/usecases where you are looking for automation and its trying to measure that end to end automation level of different models or systems. We have collected a dataset that represents documents like invoices etc which are applicable in processes where automation is needed vs are more copilot in nature where you would need to chat with document. Also have annotated these documents and published the dataset and repo so it can be extended. Here is writeup: https://ift.tt/qyvACdU Dataset: https://ift.tt/sEQR058 Github: https://ift.tt/o8Bk4F0 Looking for suggestions on how this benchmark can be improved further.
Thursday, March 13, 2025
Wednesday, March 12, 2025
New top story on Hacker News: Experiment with Gemini 2.0 Flash native image generation
Experiment with Gemini 2.0 Flash native image generation
14 by meetpateltech | 0 comments on Hacker News.
14 by meetpateltech | 0 comments on Hacker News.
Tuesday, March 11, 2025
Monday, March 10, 2025
Sunday, March 9, 2025
Saturday, March 8, 2025
Friday, March 7, 2025
Thursday, March 6, 2025
Wednesday, March 5, 2025
Tuesday, March 4, 2025
New top story on Hacker News: Show HN: Time travel debugging AI for more reliable vibe coding
Show HN: Time travel debugging AI for more reliable vibe coding
15 by bhackett | 5 comments on Hacker News.
Hi HN, I'm the CEO at https://replay.io . We've been building a time travel debugger for web apps for several years now (previous HN post: https://ift.tt/hULuFNX ) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand what's going on in the app, but AIs struggle here. With a recording of the app its behavior becomes a giant database for querying using RAG. We've been giving Claude tools to explore and understand what happens in a Replay recording, from basic stuff like seeing console messages to more advanced analysis of React, control dependencies, and dataflow. For now this is behind a chat API ( https://ift.tt/8q5TBhe ). We recently launched Nut ( https://nut.new ) as an open source project which uses this tech for building apps through prompting (vibe coding), similar to e.g. https://bolt.new and https://v0.dev . We want Nut to fix bugs effectively (cracking nuts, so to speak) and are working to make it a reliable tool for building complete production grade apps. It's been pretty neat to see Nut fixing bugs that totally stump the AI otherwise. Each of the problems below has a short video but you can also load the associated project and try it yourself. - Exception thrown from a catch block unmounts the entire app: https://ift.tt/RWPmpFS - A settings button doesn't work because its modal component isn't always created: https://ift.tt/WI3jvGC - An icon is really tiny due to sizing constraints imposed by other elements: https://ift.tt/zRwHnjq - Loading doesn't finish due to a problem initializing responsive UI state: https://ift.tt/14Nw6dI - Infinite rendering loop caused by a missing useCallback: https://ift.tt/ocQsHne Nut is completely free. You get some free uses or can add an API key, and we're also offering unlimited free access for folks who can give us feedback we'll use to improve Nut. Email me at hi@replay.io if you're interested. For now Nut is best suited for building frontends but we'll be rolling out more full stack features in the next few weeks. I'd love to know what you think!
15 by bhackett | 5 comments on Hacker News.
Hi HN, I'm the CEO at https://replay.io . We've been building a time travel debugger for web apps for several years now (previous HN post: https://ift.tt/hULuFNX ) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand what's going on in the app, but AIs struggle here. With a recording of the app its behavior becomes a giant database for querying using RAG. We've been giving Claude tools to explore and understand what happens in a Replay recording, from basic stuff like seeing console messages to more advanced analysis of React, control dependencies, and dataflow. For now this is behind a chat API ( https://ift.tt/8q5TBhe ). We recently launched Nut ( https://nut.new ) as an open source project which uses this tech for building apps through prompting (vibe coding), similar to e.g. https://bolt.new and https://v0.dev . We want Nut to fix bugs effectively (cracking nuts, so to speak) and are working to make it a reliable tool for building complete production grade apps. It's been pretty neat to see Nut fixing bugs that totally stump the AI otherwise. Each of the problems below has a short video but you can also load the associated project and try it yourself. - Exception thrown from a catch block unmounts the entire app: https://ift.tt/RWPmpFS - A settings button doesn't work because its modal component isn't always created: https://ift.tt/WI3jvGC - An icon is really tiny due to sizing constraints imposed by other elements: https://ift.tt/zRwHnjq - Loading doesn't finish due to a problem initializing responsive UI state: https://ift.tt/14Nw6dI - Infinite rendering loop caused by a missing useCallback: https://ift.tt/ocQsHne Nut is completely free. You get some free uses or can add an API key, and we're also offering unlimited free access for folks who can give us feedback we'll use to improve Nut. Email me at hi@replay.io if you're interested. For now Nut is best suited for building frontends but we'll be rolling out more full stack features in the next few weeks. I'd love to know what you think!
New top story on Hacker News: Show HN: Open-source Deep Research across workplace applications
Show HN: Open-source Deep Research across workplace applications
8 by yuhongsun | 1 comments on Hacker News.
I’ve been using deep research on OpenAI and Perplexity and it’s been just amazing at gathering data across a lot of related and chained searches. Just earlier today, I asked “What are some marquee tech companies / hot startups (not including the giants like FAAMG, Samsung, Nvidia etc.)”. It’s a pretty involved question and looking up “marquee tech startups” or "hot tech startups" on Google gave me nothing useful. Deep research on both ChatGPT and Perplexity gave really high quality responses with ChatGPT siding on slightly larger scaleups and Perplexity siding more on up and coming companies. Given how useful AI research agents are across the internet, we decided to build an open-source equivalent for the workplace since a ton of questions at work also cannot be easily resolved with a single search. Onyx supports deep research connected to company applications like Google Drive, Salesforce, Sharepoint, GitHub, Slack, and 30+ others. For example, an engineer may want to know “What’s happening with the verification email failure?” Onyx’s AI agent would first figure out what it needs to answer this question: What is the cause of the failure, what has been done to address it, has this come up before, and what’s the latest status on the issue. The agent would run parallel searches through Confluence, email, Slack, and GitHub to get the answers to these then combine them to build a coherent overview. If the agent finds that there was a technical blocker that will delay the resolution, it will adjust mid-flight and research to get more context on the blocker. Here’s a video demo I recorded: https://www.youtube.com/watch?v=drvC0fWG4hE If you want to get started with the GitHub repo, you can check out our guides at https://docs.onyx.app . Or to play with it without needing to deploy anything, you can go to https://ift.tt/osKmLXF P.S. There’s a lot of cool technical details behind building a system like this so I’ll continue the conversation in the comments.
8 by yuhongsun | 1 comments on Hacker News.
I’ve been using deep research on OpenAI and Perplexity and it’s been just amazing at gathering data across a lot of related and chained searches. Just earlier today, I asked “What are some marquee tech companies / hot startups (not including the giants like FAAMG, Samsung, Nvidia etc.)”. It’s a pretty involved question and looking up “marquee tech startups” or "hot tech startups" on Google gave me nothing useful. Deep research on both ChatGPT and Perplexity gave really high quality responses with ChatGPT siding on slightly larger scaleups and Perplexity siding more on up and coming companies. Given how useful AI research agents are across the internet, we decided to build an open-source equivalent for the workplace since a ton of questions at work also cannot be easily resolved with a single search. Onyx supports deep research connected to company applications like Google Drive, Salesforce, Sharepoint, GitHub, Slack, and 30+ others. For example, an engineer may want to know “What’s happening with the verification email failure?” Onyx’s AI agent would first figure out what it needs to answer this question: What is the cause of the failure, what has been done to address it, has this come up before, and what’s the latest status on the issue. The agent would run parallel searches through Confluence, email, Slack, and GitHub to get the answers to these then combine them to build a coherent overview. If the agent finds that there was a technical blocker that will delay the resolution, it will adjust mid-flight and research to get more context on the blocker. Here’s a video demo I recorded: https://www.youtube.com/watch?v=drvC0fWG4hE If you want to get started with the GitHub repo, you can check out our guides at https://docs.onyx.app . Or to play with it without needing to deploy anything, you can go to https://ift.tt/osKmLXF P.S. There’s a lot of cool technical details behind building a system like this so I’ll continue the conversation in the comments.
Monday, March 3, 2025
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Friday, February 28, 2025
Thursday, February 27, 2025
New top story on Hacker News: Show HN: Ranked Search for Semi-Structured Data
Show HN: Ranked Search for Semi-Structured Data
7 by alrudolph | 0 comments on Hacker News.
We’ve been working on a search problem that requires querying both text and numbers simultaneously. For example, in a dataset of clothing items with descriptions and prices, a search for “slim pants for $20” should prioritize skinny jeans for $25 over slim pants for $50 because they are semantically similar and the price is closer. I’ve found that standard embedding models struggle with numerical ordering, while text-to-SQL methods rely on exact matches and often filter out too many results. To solve this, we built a system designed specifically for structured datasets like CSVs or tables. Here’s a demo link where you can upload a small CSV to try out (no login required): https://ift.tt/1iuryvk . Unlike most RAG approaches, we process each column independently, handling text with embeddings and numbers with custom scoring. When a user submits a query, we parse it into relevant fields—for instance, extracting “slim pants” as the description and “20” as the price. We then compute cosine similarity between the description embeddings and “slim pants” while also calculating the percent error between the user’s price input and the numerical field. These individual similarity scores are then combined across all columns to generate a final ranking. Right now, our system works best with well-structured data, so some preprocessing is often needed. We’re working on improving this by detecting and restructuring messy data automatically, such as pivoting columns or extracting attributes from large text fields. We’re also adding feedback mechanisms, like a thumbs up/down system, to refine future search results based on user input. I’d love to hear about your experiences with similar search challenges and would appreciate any feedback!
7 by alrudolph | 0 comments on Hacker News.
We’ve been working on a search problem that requires querying both text and numbers simultaneously. For example, in a dataset of clothing items with descriptions and prices, a search for “slim pants for $20” should prioritize skinny jeans for $25 over slim pants for $50 because they are semantically similar and the price is closer. I’ve found that standard embedding models struggle with numerical ordering, while text-to-SQL methods rely on exact matches and often filter out too many results. To solve this, we built a system designed specifically for structured datasets like CSVs or tables. Here’s a demo link where you can upload a small CSV to try out (no login required): https://ift.tt/1iuryvk . Unlike most RAG approaches, we process each column independently, handling text with embeddings and numbers with custom scoring. When a user submits a query, we parse it into relevant fields—for instance, extracting “slim pants” as the description and “20” as the price. We then compute cosine similarity between the description embeddings and “slim pants” while also calculating the percent error between the user’s price input and the numerical field. These individual similarity scores are then combined across all columns to generate a final ranking. Right now, our system works best with well-structured data, so some preprocessing is often needed. We’re working on improving this by detecting and restructuring messy data automatically, such as pivoting columns or extracting attributes from large text fields. We’re also adding feedback mechanisms, like a thumbs up/down system, to refine future search results based on user input. I’d love to hear about your experiences with similar search challenges and would appreciate any feedback!
Wednesday, February 26, 2025
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New top story on Hacker News: (Ab)using general search algorithms on dynamic optimization problems (2023)
(Ab)using general search algorithms on dynamic optimization problems (2023)
11 by h45x1 | 3 comments on Hacker News.
I wrote this blog back in 2023 but since then I became a frequent lurker on HN and decided to repost the blog here. For me, writing it was about connecting the dots between dynamic optimization techniques I've studied as an economist and the more general search algorithms studied in CS.
11 by h45x1 | 3 comments on Hacker News.
I wrote this blog back in 2023 but since then I became a frequent lurker on HN and decided to repost the blog here. For me, writing it was about connecting the dots between dynamic optimization techniques I've studied as an economist and the more general search algorithms studied in CS.
Monday, February 17, 2025
Sunday, February 16, 2025
New top story on Hacker News: Show HN: Hackyournews.com v2
Show HN: Hackyournews.com v2
12 by ukuina | 1 comments on Hacker News.
A year and a half after I published https://ift.tt/ytrH3Uh , I've rewritten it to be neater and added support for more news sources. HackYourNews.com v1 had a great response on HN [1] and consistently sees ~2k weekly unique visitors. There were many long-standing requests that I wanted to fulfill (thanks for your patience!): a proper dark mode, correct rendering on mobile devices, and more cogent summaries. This rewrite is the result. gpt-4o-mini reduces the cost of summarization to an absurd degree, so it's now sustainable to keep this free service going! Someday, I hope to use the Batch API [2] to drive down costs even further. Enjoy. [1] https://ift.tt/dUAXTPE [2] https://ift.tt/m9SnGY2
12 by ukuina | 1 comments on Hacker News.
A year and a half after I published https://ift.tt/ytrH3Uh , I've rewritten it to be neater and added support for more news sources. HackYourNews.com v1 had a great response on HN [1] and consistently sees ~2k weekly unique visitors. There were many long-standing requests that I wanted to fulfill (thanks for your patience!): a proper dark mode, correct rendering on mobile devices, and more cogent summaries. This rewrite is the result. gpt-4o-mini reduces the cost of summarization to an absurd degree, so it's now sustainable to keep this free service going! Someday, I hope to use the Batch API [2] to drive down costs even further. Enjoy. [1] https://ift.tt/dUAXTPE [2] https://ift.tt/m9SnGY2
Saturday, February 15, 2025
Friday, February 14, 2025
Thursday, February 13, 2025
New top story on Hacker News: Phind 2: AI search with visual answers and multi-step reasoning
Phind 2: AI search with visual answers and multi-step reasoning
28 by rushingcreek | 7 comments on Hacker News.
Hi HN! Michael here. We've spent the last 6 months rebuilding Phind. We asked ourselves what types of answers we would ideally like and crafted a new UI and model series to help get us there. The new Phind goes beyond text to present answers visually with inline images, diagrams, cards, and other widgets to make answers more meaningful: - " explain photosynthesis " - https://www.youtube.com/watch?v=cTCpnyICukM#t=7 - " how to cook the perfect steak " - https://www.youtube.com/watch?v=cTCpnyICukM#t=55 - " quicksort in rust " - https://www.youtube.com/watch?v=cTCpnyICukM#t=105 Phind is also now able to seek out information on its own. If it needs more, it will do multiple rounds of additional searches to get you the most comprehensive answer it can: - " top 10 Thai restaurants in SF, their prices, and key dishes " - https://www.youtube.com/watch?v=rIQQcDIIHFQ#t=11 This blog post contains an overview of what we did as well as technical deep dives into how we built the new frontend and models. I'm super grateful for all of the feedback we've gotten from the HN community and can't wait to hear your thoughts!
28 by rushingcreek | 7 comments on Hacker News.
Hi HN! Michael here. We've spent the last 6 months rebuilding Phind. We asked ourselves what types of answers we would ideally like and crafted a new UI and model series to help get us there. The new Phind goes beyond text to present answers visually with inline images, diagrams, cards, and other widgets to make answers more meaningful: - " explain photosynthesis " - https://www.youtube.com/watch?v=cTCpnyICukM#t=7 - " how to cook the perfect steak " - https://www.youtube.com/watch?v=cTCpnyICukM#t=55 - " quicksort in rust " - https://www.youtube.com/watch?v=cTCpnyICukM#t=105 Phind is also now able to seek out information on its own. If it needs more, it will do multiple rounds of additional searches to get you the most comprehensive answer it can: - " top 10 Thai restaurants in SF, their prices, and key dishes " - https://www.youtube.com/watch?v=rIQQcDIIHFQ#t=11 This blog post contains an overview of what we did as well as technical deep dives into how we built the new frontend and models. I'm super grateful for all of the feedback we've gotten from the HN community and can't wait to hear your thoughts!
Wednesday, February 12, 2025
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New top story on Hacker News: Show HN: A website that heatmaps your city based on your housing preferences
Show HN: A website that heatmaps your city based on your housing preferences
22 by WiggleGuy | 15 comments on Hacker News.
For the past few months, I've been working on a website that answers two different questions: - Where in my city have the best travel times to all the things and people I care about? - Given a listing, how far is it from all the things and people I care about? Personally this was fueled by my own frustrations when I was apartment hunting in NYC. I was frustrating to have to juggle so many Google Maps tabs when I was evaluating a listing, and it was also annoying to not have full confidence that I was even searching in the right places. I wanted to be close to work, a Trader Joe's, and a major park. Given that public transportation networks can sometimes make close things hard to get to and far things easy to get to, it's not always obvious whether a neighborhood actually even fits my criteria or not! The overarching goal of theretowhere.com is to allow you to make more informed moving decisions while also making things more convenient than they are today. https://ibb.co/pBsX2HjN It can generate detailed travel time breakdowns for individual listings and addresses, making it easier to determine whether a listing is worth applying for without juggling Google Maps tabs. This is great for questions like “How far is this apartment from my friends, work and dancing gyms?” https://ibb.co/mVBjwPrJ It also has the powerful ability to heatmap a city based on which parts of it are close or not to the people and places you care about. This is great for questions like “Where in the city would I be reasonably close to work, friends and a woodworking studio?” https://ibb.co/vCynPSRK You can add these heatmaps to sites like Zillow and Streeteasy to make things super convenient (this was very fun to make). The main thing that's on my mind is whether this is useful or not. Like, is this something you would actually use? I also have other ideas I'd like to eventually intergrate into this (crime heatmaps, noise heatmaps, etc)
22 by WiggleGuy | 15 comments on Hacker News.
For the past few months, I've been working on a website that answers two different questions: - Where in my city have the best travel times to all the things and people I care about? - Given a listing, how far is it from all the things and people I care about? Personally this was fueled by my own frustrations when I was apartment hunting in NYC. I was frustrating to have to juggle so many Google Maps tabs when I was evaluating a listing, and it was also annoying to not have full confidence that I was even searching in the right places. I wanted to be close to work, a Trader Joe's, and a major park. Given that public transportation networks can sometimes make close things hard to get to and far things easy to get to, it's not always obvious whether a neighborhood actually even fits my criteria or not! The overarching goal of theretowhere.com is to allow you to make more informed moving decisions while also making things more convenient than they are today. https://ibb.co/pBsX2HjN It can generate detailed travel time breakdowns for individual listings and addresses, making it easier to determine whether a listing is worth applying for without juggling Google Maps tabs. This is great for questions like “How far is this apartment from my friends, work and dancing gyms?” https://ibb.co/mVBjwPrJ It also has the powerful ability to heatmap a city based on which parts of it are close or not to the people and places you care about. This is great for questions like “Where in the city would I be reasonably close to work, friends and a woodworking studio?” https://ibb.co/vCynPSRK You can add these heatmaps to sites like Zillow and Streeteasy to make things super convenient (this was very fun to make). The main thing that's on my mind is whether this is useful or not. Like, is this something you would actually use? I also have other ideas I'd like to eventually intergrate into this (crime heatmaps, noise heatmaps, etc)
Thursday, February 6, 2025
New top story on Hacker News: Show HN: Heap Explorer
Show HN: Heap Explorer
10 by bkallus | 0 comments on Hacker News.
I wrote a little LD_PRELOAD library that makes it easy to inspect and interact with a running program's glibc heap. It's fun to pause processes, free a bunch of their allocations, then resume them. Most of the time, the processes continue as though nothing happened, but sometimes they do interesting things :)
10 by bkallus | 0 comments on Hacker News.
I wrote a little LD_PRELOAD library that makes it easy to inspect and interact with a running program's glibc heap. It's fun to pause processes, free a bunch of their allocations, then resume them. Most of the time, the processes continue as though nothing happened, but sometimes they do interesting things :)
Wednesday, February 5, 2025
Tuesday, February 4, 2025
Monday, February 3, 2025
New top story on Hacker News: Ask HN: Who wants to be hired? (February 2025)
Ask HN: Who wants to be hired? (February 2025)
28 by whoishiring | 105 comments on Hacker News.
Share your information if you are looking for work. Please use this format: Location: Remote: Willing to relocate: Technologies: Résumé/CV: Email: Please only post if you are personally looking for work. Agencies, recruiters, job boards, and so on, are off topic here. Readers: please only email these addresses to discuss work opportunities. There's a site for searching these posts at https://ift.tt/USp0PTR .
28 by whoishiring | 105 comments on Hacker News.
Share your information if you are looking for work. Please use this format: Location: Remote: Willing to relocate: Technologies: Résumé/CV: Email: Please only post if you are personally looking for work. Agencies, recruiters, job boards, and so on, are off topic here. Readers: please only email these addresses to discuss work opportunities. There's a site for searching these posts at https://ift.tt/USp0PTR .
Sunday, February 2, 2025
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Thursday, January 30, 2025
New top story on Hacker News: Show HN: Distr – open-source distribution platform for on-prem deployments
Show HN: Distr – open-source distribution platform for on-prem deployments
13 by louis_w_gk | 0 comments on Hacker News.
Distr is designed to help software engineers distribute and manage their applications or agents in customer-controlled or shared-responsibility environments. You only need a Docker Compose file or Helm chart—everything else for on-prem is handled by the platform. We’re are an open source dev tool company. Over the past couple of months, we’ve spoken with dozens of software companies to understand their challenges with on-prem deployments. We analyzed the internal tools they’ve built and the best practices from existing solutions, combining them into a prebuilt, Open Source solution that works out of the box and integrates seamlessly. Distr consists of two key components: 1. Hub - Provides a centralized view of all deployments and controls connected agents. - Comes with a simple GUI but also supports API and SDK access for seamless integration. - Fully Open- Surce and self-hostable, or you can use our fully managed platform. 2. Lightweight Agents - Pre-built agents for Helm (Kubernetes) and Docker Compose (VM) that run alongside your application. - Handle lifecycle tasks like guided installation, updates, and rollbacks. - Provide basic metrics (health status, application version) and logs If you already have a customer portal or self-service interface for on-prem deployments, you can seamlessly integrate all features into your existing portal or application using our API or SDK. Alternatively, you can use our pre-built, white-labeled customer portal. Here’s what an integration into your existing customer portal could look like: import {DistrService} from "@glasskube/distr-sdk"; const customerHasAutoUpdatesEnabled = false; // replace with your own logic const deploymentTargetId = 'da1d7130-bfa9-49a1-b567-c49728837df7'; const service = new DistrService({ apiKey: 'distr-8c24167aeb5fd4bb48b6d2140927df0f' }); const result = await service.isOutdated(deploymentTargetId); if(result.deploymentTarget.deployment?.latestStatus?.type !== 'ok') { // let the user decide whether to allow updates from an instable state, e.g. with: if(!confirm('The deployment is not in a stable state. Do you want to update anyway?')) { return; } } if(result.outdated) { if(customerHasAutoUpdatesEnabled) { await service.updateDeployment({deploymentTargetId}); // notify customer about the update } else { const newerVersionsAvailable = result.newerVersions; // notify customer about the newer versions, e.g. via email } } With the SDK/API, you can: - Display real-time deployed version and deployment status directly within the application, notifying customers when their deployed version is outdated. - Allow customers to trigger updates from within your app using a simple API call If you’re distributing software and want to streamline updates or enhance monitoring, we’d love your feedback and are here to answer any questions. Getting started is easy—just bring your Docker Compose file or Helm chart, and we’ll guide you through the rest. Check out the fully managed version ( https://ift.tt/NtBEzfT ) and explore our documentation ( https://distr.sh/docs/ ) to learn more.
13 by louis_w_gk | 0 comments on Hacker News.
Distr is designed to help software engineers distribute and manage their applications or agents in customer-controlled or shared-responsibility environments. You only need a Docker Compose file or Helm chart—everything else for on-prem is handled by the platform. We’re are an open source dev tool company. Over the past couple of months, we’ve spoken with dozens of software companies to understand their challenges with on-prem deployments. We analyzed the internal tools they’ve built and the best practices from existing solutions, combining them into a prebuilt, Open Source solution that works out of the box and integrates seamlessly. Distr consists of two key components: 1. Hub - Provides a centralized view of all deployments and controls connected agents. - Comes with a simple GUI but also supports API and SDK access for seamless integration. - Fully Open- Surce and self-hostable, or you can use our fully managed platform. 2. Lightweight Agents - Pre-built agents for Helm (Kubernetes) and Docker Compose (VM) that run alongside your application. - Handle lifecycle tasks like guided installation, updates, and rollbacks. - Provide basic metrics (health status, application version) and logs If you already have a customer portal or self-service interface for on-prem deployments, you can seamlessly integrate all features into your existing portal or application using our API or SDK. Alternatively, you can use our pre-built, white-labeled customer portal. Here’s what an integration into your existing customer portal could look like: import {DistrService} from "@glasskube/distr-sdk"; const customerHasAutoUpdatesEnabled = false; // replace with your own logic const deploymentTargetId = 'da1d7130-bfa9-49a1-b567-c49728837df7'; const service = new DistrService({ apiKey: 'distr-8c24167aeb5fd4bb48b6d2140927df0f' }); const result = await service.isOutdated(deploymentTargetId); if(result.deploymentTarget.deployment?.latestStatus?.type !== 'ok') { // let the user decide whether to allow updates from an instable state, e.g. with: if(!confirm('The deployment is not in a stable state. Do you want to update anyway?')) { return; } } if(result.outdated) { if(customerHasAutoUpdatesEnabled) { await service.updateDeployment({deploymentTargetId}); // notify customer about the update } else { const newerVersionsAvailable = result.newerVersions; // notify customer about the newer versions, e.g. via email } } With the SDK/API, you can: - Display real-time deployed version and deployment status directly within the application, notifying customers when their deployed version is outdated. - Allow customers to trigger updates from within your app using a simple API call If you’re distributing software and want to streamline updates or enhance monitoring, we’d love your feedback and are here to answer any questions. Getting started is easy—just bring your Docker Compose file or Helm chart, and we’ll guide you through the rest. Check out the fully managed version ( https://ift.tt/NtBEzfT ) and explore our documentation ( https://distr.sh/docs/ ) to learn more.
Wednesday, January 29, 2025
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Thursday, January 23, 2025
New top story on Hacker News: Show HN: I built an active community of trans people online
Show HN: I built an active community of trans people online
58 by t4t | 22 comments on Hacker News.
A year ago I surveyed the internet and noticed there was only one popular space for trans and gender-non-conforming people to meet; Lex. Lex is not well liked by its users. Its software feels heavy and it is full of cash grabs and anti-patterns. It was recently acquired and is sure to only become more hostile to its users as it turns towards profit generation. With this in mind I built t4t, an alternative specially designed for not only queer people, but specifically trans people. It is an extremely lightweight service. I built it with my most ideal stack: Flutter, Svelte, Supabase, Posthog. It has grown in the last year to about 4,000 monthly active users. I think it could grow way beyond that this year.
58 by t4t | 22 comments on Hacker News.
A year ago I surveyed the internet and noticed there was only one popular space for trans and gender-non-conforming people to meet; Lex. Lex is not well liked by its users. Its software feels heavy and it is full of cash grabs and anti-patterns. It was recently acquired and is sure to only become more hostile to its users as it turns towards profit generation. With this in mind I built t4t, an alternative specially designed for not only queer people, but specifically trans people. It is an extremely lightweight service. I built it with my most ideal stack: Flutter, Svelte, Supabase, Posthog. It has grown in the last year to about 4,000 monthly active users. I think it could grow way beyond that this year.
Wednesday, January 22, 2025
New top story on Hacker News: Show HN: I Made an Open-Source Laptop from Scratch
Show HN: I Made an Open-Source Laptop from Scratch
34 by Hello9999901 | 5 comments on Hacker News.
Hello! I'm Byran. I spent the past ~6 months engineering a laptop from scratch. It's fully open-source on GH at: https://ift.tt/CnAS6PX
34 by Hello9999901 | 5 comments on Hacker News.
Hello! I'm Byran. I spent the past ~6 months engineering a laptop from scratch. It's fully open-source on GH at: https://ift.tt/CnAS6PX
Tuesday, January 21, 2025
New top story on Hacker News: Show HN: SudokuVariants – play and construct different variants of Sudoku
Show HN: SudokuVariants – play and construct different variants of Sudoku
10 by stanac | 4 comments on Hacker News.
Hi HN, I've been working on this Sudoku web app for the past couple of years, on and off during free weekends and afternoons. I started working on it because I was bored during COVID, and Cracking the Cryptic had just become popular on YouTube, which got me wondering how hard it could be to make a Sudoku app. The main idea is for the app to understand the constraints and know how to solve Sudoku grids (and not just be a simple Sudoku drawing/playing app). When it comes to classic Sudoku, the solver doesn't support anything more complicated than X-Wing, but it understands the constraints. At the moment, most of the popular variants are supported: killer, sandwich, arrow, thermo, palindrome, German whisper, kropki, consecutive, non-consecutive, greater than, XV, diagonal, anti-king, anti-knight, even-odd, windoku, renban, and zipper. The only variant I am yet to add support for is quadruple. If any other variant becomes popular, I will probably add it, as was the case with zipper lines during development. A user account is not required to play, but it is required if you want to publish a public grid on the app. The app doesn't collect any PII, doesn't have ads or trackers. Accounts are identified by email hash; I am not storing email addresses or passwords, and OTPs are sent by email. The less I know about users, the better for both sides. The app supports mobile devices, but it works best on bigger screens. It was built using Blazor SSR/WASM (AOT) with SVG for interactive parts. I know there are some performance issues (especially on mobile phones and with touch input), and I am trying to address them. Some of the features I was thinking about adding are classifying grids by difficulty, daily Sudoku, and maybe campaigns (groups of Sudoku grids where users have to solve them in order). If you like Sudoku, or more specifically variants of Sudoku, please let me know what you think about SudokuVariants. URL: https://ift.tt/qRCHuvg Thanks!
10 by stanac | 4 comments on Hacker News.
Hi HN, I've been working on this Sudoku web app for the past couple of years, on and off during free weekends and afternoons. I started working on it because I was bored during COVID, and Cracking the Cryptic had just become popular on YouTube, which got me wondering how hard it could be to make a Sudoku app. The main idea is for the app to understand the constraints and know how to solve Sudoku grids (and not just be a simple Sudoku drawing/playing app). When it comes to classic Sudoku, the solver doesn't support anything more complicated than X-Wing, but it understands the constraints. At the moment, most of the popular variants are supported: killer, sandwich, arrow, thermo, palindrome, German whisper, kropki, consecutive, non-consecutive, greater than, XV, diagonal, anti-king, anti-knight, even-odd, windoku, renban, and zipper. The only variant I am yet to add support for is quadruple. If any other variant becomes popular, I will probably add it, as was the case with zipper lines during development. A user account is not required to play, but it is required if you want to publish a public grid on the app. The app doesn't collect any PII, doesn't have ads or trackers. Accounts are identified by email hash; I am not storing email addresses or passwords, and OTPs are sent by email. The less I know about users, the better for both sides. The app supports mobile devices, but it works best on bigger screens. It was built using Blazor SSR/WASM (AOT) with SVG for interactive parts. I know there are some performance issues (especially on mobile phones and with touch input), and I am trying to address them. Some of the features I was thinking about adding are classifying grids by difficulty, daily Sudoku, and maybe campaigns (groups of Sudoku grids where users have to solve them in order). If you like Sudoku, or more specifically variants of Sudoku, please let me know what you think about SudokuVariants. URL: https://ift.tt/qRCHuvg Thanks!
Monday, January 20, 2025
Sunday, January 19, 2025
Saturday, January 18, 2025
New top story on Hacker News: Show HN: ZX Spectrum SCR to PNG Converter
Show HN: ZX Spectrum SCR to PNG Converter
3 by iamflimflam1 | 0 comments on Hacker News.
Scratching my own itch. I had to do this for showing information on ZX Spectrum games. So thought I'd turn it into a useful tool for other people to use.
3 by iamflimflam1 | 0 comments on Hacker News.
Scratching my own itch. I had to do this for showing information on ZX Spectrum games. So thought I'd turn it into a useful tool for other people to use.
Friday, January 17, 2025
New top story on Hacker News: Dr. TVAM – Inverse Rendering for Tomographic Volumetric Additive Manufacturing
Dr. TVAM – Inverse Rendering for Tomographic Volumetric Additive Manufacturing
8 by roflmaostc | 0 comments on Hacker News.
We published this work at SIGGRAPH ASIA 2024. Based on Mitsuba 3 and Dr. JIT we provide our software Dr. TVAM to optimize patterns for TVAM. TVAM allows to print centi-meter scale objects within seconds.
8 by roflmaostc | 0 comments on Hacker News.
We published this work at SIGGRAPH ASIA 2024. Based on Mitsuba 3 and Dr. JIT we provide our software Dr. TVAM to optimize patterns for TVAM. TVAM allows to print centi-meter scale objects within seconds.
Thursday, January 16, 2025
Wednesday, January 15, 2025
New top story on Hacker News: Show HN: I Built a Fair Alternative to Product Hunt for Indie Makers Like You
Show HN: I Built a Fair Alternative to Product Hunt for Indie Makers Like You
10 by lakshikag | 3 comments on Hacker News.
I’m an indie maker, just like many of you. A few months back, I launched a product on one of the big platforms, and... nothing. It got buried under dozens of other launches within hours. All that work, all that excitement is gone in the blink of an eye. No one even saw it. It stung. I wasn’t mad, well, maybe a little but mostly, I just felt invisible. The truth is, indie makers like me don’t have big teams or budgets to fight for visibility. We rely on genuine support and connections. I couldn’t stop thinking about how many great ideas never get the attention they deserve because they’re overshadowed. So, I decided to build something different: https://itslaunched.com Here’s the idea: • 10 launches per day, max. Limiting the number of daily launches ensures that every product gets its moment in the spotlight. • 2 votes per user, per day. This isn’t a popularity contest. You only get two votes, so people have to really think about which products they want to support. It’s quality over quantity. • “Under Radar” feature. This one’s my favorite. If a product doesn’t get much love on its launch day, it gets a second chance to shine the next day. Because timing shouldn’t be the only thing standing between you and success. There’s more like badges, comments, streaks but the heart of it is simple: a fair shot for indie makers. I built this because I believe every product deserves to be seen, especially the ones built by solo makers and small teams putting their heart into something they truly care about. And I didn’t build this to compete with Product Hunt. I built it to give indie makers the platform they deserve, one where their creativity truly gets noticed. If this sounds like something you’d want to check out, I’d love your thoughts. I’m still tweaking and improving it every day based on feedback. Let me know what you think and if you’ve got a product you’re proud of, I’d love to see it shine.
10 by lakshikag | 3 comments on Hacker News.
I’m an indie maker, just like many of you. A few months back, I launched a product on one of the big platforms, and... nothing. It got buried under dozens of other launches within hours. All that work, all that excitement is gone in the blink of an eye. No one even saw it. It stung. I wasn’t mad, well, maybe a little but mostly, I just felt invisible. The truth is, indie makers like me don’t have big teams or budgets to fight for visibility. We rely on genuine support and connections. I couldn’t stop thinking about how many great ideas never get the attention they deserve because they’re overshadowed. So, I decided to build something different: https://itslaunched.com Here’s the idea: • 10 launches per day, max. Limiting the number of daily launches ensures that every product gets its moment in the spotlight. • 2 votes per user, per day. This isn’t a popularity contest. You only get two votes, so people have to really think about which products they want to support. It’s quality over quantity. • “Under Radar” feature. This one’s my favorite. If a product doesn’t get much love on its launch day, it gets a second chance to shine the next day. Because timing shouldn’t be the only thing standing between you and success. There’s more like badges, comments, streaks but the heart of it is simple: a fair shot for indie makers. I built this because I believe every product deserves to be seen, especially the ones built by solo makers and small teams putting their heart into something they truly care about. And I didn’t build this to compete with Product Hunt. I built it to give indie makers the platform they deserve, one where their creativity truly gets noticed. If this sounds like something you’d want to check out, I’d love your thoughts. I’m still tweaking and improving it every day based on feedback. Let me know what you think and if you’ve got a product you’re proud of, I’d love to see it shine.
Tuesday, January 14, 2025
Monday, January 13, 2025
Sunday, January 12, 2025
Saturday, January 11, 2025
Friday, January 10, 2025
Thursday, January 9, 2025
New top story on Hacker News: Show HN: TabPFN v2 – A SOTA foundation model for small tabular data
Show HN: TabPFN v2 – A SOTA foundation model for small tabular data
19 by onasta | 2 comments on Hacker News.
I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: https://ift.tt/kGjnro0 . You can also try it via API: https://ift.tt/DvXYl7f TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation. Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets. TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data. We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better. There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus. We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems. We would love for you to try out TabPFN v2 and give us your feedback!
19 by onasta | 2 comments on Hacker News.
I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license: https://ift.tt/kGjnro0 . You can also try it via API: https://ift.tt/DvXYl7f TabPFN v2 is trained on 130 million synthetic tabular prediction datasets to perform in-context learning and output a predictive distribution for the test data points. Each dataset acts as one meta-datapoint to train the TabPFN weights with SGD. As a foundation model, TabPFN allows for fine-tuning, density estimation and data generation. Compared to TabPFN v1, v2 now natively supports categorical features and missing values. TabPFN v2 performs just as well on datasets with or without these. It also handles outliers and uninformative features naturally, problems that often throw off standard neural nets. TabPFN v2 performs as well with half the data as the next best baseline (CatBoost) with all the data. We also compared TabPFN to the SOTA AutoML system AutoGluon 1.0. Standard TabPFN already outperforms AutoGluon on classification and ties on regression, but ensembling multiple TabPFNs in TabPFN v2 (PHE) is even better. There are some limitations: TabPFN v2 is very fast to train and does not require hyperparameter tuning, but inference is slow. The model is also only designed for datasets up to 10k data points and 500 features. While it may perform well on larger datasets, it hasn't been our focus. We're actively working on removing these limitations and intend to release new versions of TabPFN that can handle larger datasets, have faster inference and perform in additional predictive settings such as time-series and recommender systems. We would love for you to try out TabPFN v2 and give us your feedback!
Wednesday, January 8, 2025
Tuesday, January 7, 2025
Monday, January 6, 2025
Sunday, January 5, 2025
Saturday, January 4, 2025
New top story on Hacker News: Show HN: Open Rewind – POC for audio and screen and video streaming to S3
Show HN: Open Rewind – POC for audio and screen and video streaming to S3
10 by wwoessi | 1 comments on Hacker News.
Got into a rabbit hole today. POC works using 'npx efficient-recorder'. Is this useful to anyone?
10 by wwoessi | 1 comments on Hacker News.
Got into a rabbit hole today. POC works using 'npx efficient-recorder'. Is this useful to anyone?
Friday, January 3, 2025
Thursday, January 2, 2025
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