Stewart Cheifet, creator of The Computer Chronicles, dead at 87
43 by spankibalt | 20 comments on Hacker News.
Wednesday, December 31, 2025
Tuesday, December 30, 2025
Monday, December 29, 2025
New top story on Hacker News: Show HN: Per-instance TSP Solver with No Pre-training (1.66% gap on d1291)
Show HN: Per-instance TSP Solver with No Pre-training (1.66% gap on d1291)
7 by jivaprime | 0 comments on Hacker News.
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error. It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis. Code & Colab: https://ift.tt/hb4zfGF Happy to answer any questions about the geometric priors or the PPO implementation!
7 by jivaprime | 0 comments on Hacker News.
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error. It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis. Code & Colab: https://ift.tt/hb4zfGF Happy to answer any questions about the geometric priors or the PPO implementation!
Sunday, December 28, 2025
Saturday, December 27, 2025
Friday, December 26, 2025
Thursday, December 25, 2025
New top story on Hacker News: Show HN: Lamp Carousel – DIY Kinetic Sculpture Powered by Lamp Heat
Show HN: Lamp Carousel – DIY Kinetic Sculpture Powered by Lamp Heat
3 by Evidlo | 1 comments on Hacker News.
I wanted to share this fun craft activity for the holidays that I've been doing with my family over the last few years. I came up with these while cutting up some cans trying to make an aluminum version of paper spinners. There are a variety of shapes that work, but generally bigger+lighter spinners are better. Also incandescent bulbs are the best, but LEDs work too. They remind me of candle carousels I would see at my grandparents' house during Christmas. Let me know what you think!
3 by Evidlo | 1 comments on Hacker News.
I wanted to share this fun craft activity for the holidays that I've been doing with my family over the last few years. I came up with these while cutting up some cans trying to make an aluminum version of paper spinners. There are a variety of shapes that work, but generally bigger+lighter spinners are better. Also incandescent bulbs are the best, but LEDs work too. They remind me of candle carousels I would see at my grandparents' house during Christmas. Let me know what you think!
Wednesday, December 24, 2025
Tuesday, December 23, 2025
Monday, December 22, 2025
Sunday, December 21, 2025
Saturday, December 20, 2025
New top story on Hacker News: Show HN: HN Wrapped 2025 - an LLM reviews your year on HN
Show HN: HN Wrapped 2025 - an LLM reviews your year on HN
10 by hubraumhugo | 3 comments on Hacker News.
I was looking for some fun project to play around with the latest Gemini models and ended up building this :) Enter your username and get: - Generated roasts and stats based on your HN activity 2025 - Your personalized HN front page from 2035 (inspired by a recent Show HN [0]) - An xkcd-style comic of your HN persona It uses the latest gemini-3-flash and gemini-3-pro-image (nano banana pro) models, which deliver pretty impressive and funny results. A few examples: - dang: https://ift.tt/W8CZm9o - myself: https://ift.tt/FcRVfoW Give it a try and share yours :) Happy holidays! [0] https://ift.tt/5ciXY79
10 by hubraumhugo | 3 comments on Hacker News.
I was looking for some fun project to play around with the latest Gemini models and ended up building this :) Enter your username and get: - Generated roasts and stats based on your HN activity 2025 - Your personalized HN front page from 2035 (inspired by a recent Show HN [0]) - An xkcd-style comic of your HN persona It uses the latest gemini-3-flash and gemini-3-pro-image (nano banana pro) models, which deliver pretty impressive and funny results. A few examples: - dang: https://ift.tt/W8CZm9o - myself: https://ift.tt/FcRVfoW Give it a try and share yours :) Happy holidays! [0] https://ift.tt/5ciXY79
Friday, December 19, 2025
New top story on Hacker News: Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude)
Show HN: Linggen – A local-first memory layer for your AI (Cursor, Zed, Claude)
3 by linggen | 2 comments on Hacker News.
Hi HN, Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this. My Workflow: I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem. The Tech: Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No accounts required. Team Memory: Index knowledge so teammates' LLMs get context automatically. Visual Map: See file dependencies and refactor "blast radius." MCP-Native: Supports Cursor, Zed, and Claude Desktop. Linggen saves me hours. I’d love to hear how you manage complex system context! Repo: https://ift.tt/Pbd0pj7 Website: https://linggen.dev
3 by linggen | 2 comments on Hacker News.
Hi HN, Working with multiple projects, I got tired of re-explaining our complex multi-node system to LLMs. Documentation helped, but plain text is hard to search without indexing and doesn't work across projects. I built Linggen to solve this. My Workflow: I use the Linggen VS Code extension to "init my day." It calls the Linggen MCP to load memory instantly. Linggen indexes all my docs like it’s remembering them—it is awesome. One click loads the full architectural context, removing the "cold start" problem. The Tech: Local-First: Rust + LanceDB. Code and embeddings stay on your machine. No accounts required. Team Memory: Index knowledge so teammates' LLMs get context automatically. Visual Map: See file dependencies and refactor "blast radius." MCP-Native: Supports Cursor, Zed, and Claude Desktop. Linggen saves me hours. I’d love to hear how you manage complex system context! Repo: https://ift.tt/Pbd0pj7 Website: https://linggen.dev
Thursday, December 18, 2025
New top story on Hacker News: Show HN: Paper2Any – Open tool to generate editable PPTs from research papers
Show HN: Paper2Any – Open tool to generate editable PPTs from research papers
6 by Mey0320 | 0 comments on Hacker News.
Hi HN, We are the OpenDCAI group from Peking University. We built Paper2Any, an open-source tool designed to automate the "Paper to Slides" workflow based on our DataFlow-Agent framework. The Problem: Writing papers is hard, but creating professional architecture diagrams and slides (PPTs) is often more tedious. Most AI tools just generate static images (PNGs) that are impossible to tweak for final publication. The Solution: Paper2Any takes a PDF, text, or sketch as input, understands the research logic, and generates fully editable PPTX (PowerPoint) files and SVGs. We prioritize flexibility and fidelity—allowing you to specify page ranges, switch visual styles, and preserve original assets. How it works: 1. Multimodal Reading: Extracts text and visual elements from the paper. You can now specify page ranges (e.g., Method section only) to focus the context and reduce token usage. 2. Content Understanding: Identifies core contributions and structural logic. 3. PPT Generation: Instead of generating one flat image, it generates independent elements (blocks, arrows, text) with selectable visual styles and organizes them into a slide layout. Links: - Demo: http://dcai-paper2any.cpolar.top/ - Code (DataFlow-Agent): https://ift.tt/HIpOKhB We'd love to hear your feedback on the generation quality and the agent workflow!
6 by Mey0320 | 0 comments on Hacker News.
Hi HN, We are the OpenDCAI group from Peking University. We built Paper2Any, an open-source tool designed to automate the "Paper to Slides" workflow based on our DataFlow-Agent framework. The Problem: Writing papers is hard, but creating professional architecture diagrams and slides (PPTs) is often more tedious. Most AI tools just generate static images (PNGs) that are impossible to tweak for final publication. The Solution: Paper2Any takes a PDF, text, or sketch as input, understands the research logic, and generates fully editable PPTX (PowerPoint) files and SVGs. We prioritize flexibility and fidelity—allowing you to specify page ranges, switch visual styles, and preserve original assets. How it works: 1. Multimodal Reading: Extracts text and visual elements from the paper. You can now specify page ranges (e.g., Method section only) to focus the context and reduce token usage. 2. Content Understanding: Identifies core contributions and structural logic. 3. PPT Generation: Instead of generating one flat image, it generates independent elements (blocks, arrows, text) with selectable visual styles and organizes them into a slide layout. Links: - Demo: http://dcai-paper2any.cpolar.top/ - Code (DataFlow-Agent): https://ift.tt/HIpOKhB We'd love to hear your feedback on the generation quality and the agent workflow!
Wednesday, December 17, 2025
Tuesday, December 16, 2025
Monday, December 15, 2025
Sunday, December 14, 2025
Saturday, December 13, 2025
Friday, December 12, 2025
Thursday, December 11, 2025
New top story on Hacker News: Show HN: SIM – Apache-2.0 n8n alternative
Show HN: SIM – Apache-2.0 n8n alternative
17 by waleedlatif1 | 0 comments on Hacker News.
Hey HN, Waleed here. We're building Sim ( https://sim.ai/ ), an open-source visual editor to build agentic workflows. Repo here: https://ift.tt/mFD0WzL . Docs here: https://docs.sim.ai . You can run Sim locally using Docker, with no execution limits or other restrictions. We started building Sim almost a year ago after repeatedly troubleshooting why our agents failed in production. Code-first frameworks felt hard to debug because of implicit control flow, and workflow platforms added more overhead than they removed. We wanted granular control and easy observability without piecing everything together ourselves. We launched Sim [1][2] as a drag-and-drop canvas around 6 months ago. Since then, we've added: - 138 blocks: Slack, GitHub, Linear, Notion, Supabase, SSH, TTS, SFTP, MongoDB, S3, Pinecone, ... - Tool calling with granular control: forced, auto - Agent memory: conversation memory with sliding window support (by last n messages or tokens) - Trace spans: detailed logging and observability for nested workflows and tool calling - Native RAG: upload documents, we chunk, embed with pgvector, and expose vector search to agents - Workflow deployment versioning with rollbacks - MCP support, Human-in-the-loop block - Copilot to build workflows using natural language (just shipped a new version that also acts as a superagent and can call into any of your connected services directly, not just build workflows) Under the hood, the workflow is a DAG with concurrent execution by default. Nodes run as soon as their dependencies (upstream blocks) are satisfied. Loops (for, forEach, while, do-while) and parallel fan-out/join are also first-class primitives. Agent blocks are pass-through to the provider. You pick your model (OpenAI, Anthropic, Gemini, Ollama, vLLM), and and we pass through prompts, tools, and response format directly to the provider API. We normalize response shapes for block interoperability, but we're not adding layers that obscure what's happening. We're currently working on our own MCP server and the ability to deploy workflows as MCP servers. Would love to hear your thoughts and where we should take it next :) [1] https://ift.tt/2Wh6bRa [2] https://ift.tt/ECsgvuH
17 by waleedlatif1 | 0 comments on Hacker News.
Hey HN, Waleed here. We're building Sim ( https://sim.ai/ ), an open-source visual editor to build agentic workflows. Repo here: https://ift.tt/mFD0WzL . Docs here: https://docs.sim.ai . You can run Sim locally using Docker, with no execution limits or other restrictions. We started building Sim almost a year ago after repeatedly troubleshooting why our agents failed in production. Code-first frameworks felt hard to debug because of implicit control flow, and workflow platforms added more overhead than they removed. We wanted granular control and easy observability without piecing everything together ourselves. We launched Sim [1][2] as a drag-and-drop canvas around 6 months ago. Since then, we've added: - 138 blocks: Slack, GitHub, Linear, Notion, Supabase, SSH, TTS, SFTP, MongoDB, S3, Pinecone, ... - Tool calling with granular control: forced, auto - Agent memory: conversation memory with sliding window support (by last n messages or tokens) - Trace spans: detailed logging and observability for nested workflows and tool calling - Native RAG: upload documents, we chunk, embed with pgvector, and expose vector search to agents - Workflow deployment versioning with rollbacks - MCP support, Human-in-the-loop block - Copilot to build workflows using natural language (just shipped a new version that also acts as a superagent and can call into any of your connected services directly, not just build workflows) Under the hood, the workflow is a DAG with concurrent execution by default. Nodes run as soon as their dependencies (upstream blocks) are satisfied. Loops (for, forEach, while, do-while) and parallel fan-out/join are also first-class primitives. Agent blocks are pass-through to the provider. You pick your model (OpenAI, Anthropic, Gemini, Ollama, vLLM), and and we pass through prompts, tools, and response format directly to the provider API. We normalize response shapes for block interoperability, but we're not adding layers that obscure what's happening. We're currently working on our own MCP server and the ability to deploy workflows as MCP servers. Would love to hear your thoughts and where we should take it next :) [1] https://ift.tt/2Wh6bRa [2] https://ift.tt/ECsgvuH
Wednesday, December 10, 2025
New top story on Hacker News: Show HN: Automated License Plate Reader Coverage in the USA
Show HN: Automated License Plate Reader Coverage in the USA
3 by sodality2 | 1 comments on Hacker News.
Built this over the last few days, based on a Rust codebase that parses the latest ALPR reports from OpenStreetMaps, calculates navigation statistics from every tagged residential building to nearby amenities, and tests each route for intersection with those ALPR cameras (Flock being the most widespread). These have gotten more controversial in recent months, due to their indiscriminate large scale data collection, with 404 Media publishing many original pieces ( https://ift.tt/Fz8QM7j ) about their adoption and (ab)use across the country. I wanted to use open source datasets to track the rapid expansion, especially per-county, as this data can be crucial for 'deflock' movements to petition counties and city governments to ban and remove them. In some counties, the tracking becomes so widespread that most people can't go anywhere without being photographed. This includes possibly sensitive areas, like places of worship and medical facilities. The argument for their legality rests upon the notion that these cameras are equivalent to 'mere observation', but the enormous scope and data sharing agreements in place to share and access millions of records without warrants blurs the lines of the fourth amendment.
3 by sodality2 | 1 comments on Hacker News.
Built this over the last few days, based on a Rust codebase that parses the latest ALPR reports from OpenStreetMaps, calculates navigation statistics from every tagged residential building to nearby amenities, and tests each route for intersection with those ALPR cameras (Flock being the most widespread). These have gotten more controversial in recent months, due to their indiscriminate large scale data collection, with 404 Media publishing many original pieces ( https://ift.tt/Fz8QM7j ) about their adoption and (ab)use across the country. I wanted to use open source datasets to track the rapid expansion, especially per-county, as this data can be crucial for 'deflock' movements to petition counties and city governments to ban and remove them. In some counties, the tracking becomes so widespread that most people can't go anywhere without being photographed. This includes possibly sensitive areas, like places of worship and medical facilities. The argument for their legality rests upon the notion that these cameras are equivalent to 'mere observation', but the enormous scope and data sharing agreements in place to share and access millions of records without warrants blurs the lines of the fourth amendment.
Tuesday, December 9, 2025
Monday, December 8, 2025
New top story on Hacker News: Paramount launches hostile bid for Warner Bros
Paramount launches hostile bid for Warner Bros
115 by gniting | 94 comments on Hacker News.
Previously: Netflix to Acquire Warner Bros - https://ift.tt/pGlzTQU (1333 comments)
115 by gniting | 94 comments on Hacker News.
Previously: Netflix to Acquire Warner Bros - https://ift.tt/pGlzTQU (1333 comments)
Sunday, December 7, 2025
Saturday, December 6, 2025
Friday, December 5, 2025
Thursday, December 4, 2025
Wednesday, December 3, 2025
New top story on Hacker News: Valve reveals it’s the architect behind a push to bring Windows games to Arm
Valve reveals it’s the architect behind a push to bring Windows games to Arm
80 by evolve2k | 199 comments on Hacker News.
https://ift.tt/2saLVGv
80 by evolve2k | 199 comments on Hacker News.
https://ift.tt/2saLVGv
Tuesday, December 2, 2025
Monday, December 1, 2025
Sunday, November 30, 2025
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Wednesday, November 19, 2025
New top story on Hacker News: Show HN: Vibe Prolog
Show HN: Vibe Prolog
10 by nl | 0 comments on Hacker News.
Like a lot of people I got the $250 Claude Code credit and didn't use it up. I decided to try to use it up over the weekend using (mostly) my phone and vibe coded a Prolog interpreter. Now I'm seeing how far I can push it.
10 by nl | 0 comments on Hacker News.
Like a lot of people I got the $250 Claude Code credit and didn't use it up. I decided to try to use it up over the weekend using (mostly) my phone and vibe coded a Prolog interpreter. Now I'm seeing how far I can push it.
Tuesday, November 18, 2025
Monday, November 17, 2025
Sunday, November 16, 2025
Saturday, November 15, 2025
Friday, November 14, 2025
Thursday, November 13, 2025
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Tuesday, November 4, 2025
Sunday, November 2, 2025
Saturday, November 1, 2025
New top story on Hacker News: Nutella maker in hazelnut stand-off with Turkish dealers
Nutella maker in hazelnut stand-off with Turkish dealers
30 by bookofjoe | 17 comments on Hacker News.
https://ift.tt/St5mq8O
30 by bookofjoe | 17 comments on Hacker News.
https://ift.tt/St5mq8O
Friday, October 31, 2025
New top story on Hacker News: Futurelock: A subtle risk in async Rust
Futurelock: A subtle risk in async Rust
28 by bcantrill | 0 comments on Hacker News.
This RFD describes our distillation of a really gnarly issue that we hit in the Oxide control plane.[0] Not unlike our discovery of the async cancellation issue[1][2][3], this is larger than the issue itself -- and worse, the program that hits futurelock is correct from the programmer's point of view. Fortunately, the surface area here is smaller than that of async cancellation and the conditions required to hit it can be relatively easily mitigated. Still, this is a pretty deep issue -- and something that took some very seasoned Rust hands quite a while to find. [0] https://ift.tt/tOr7IlP [1] https://ift.tt/h3jp7iL [2] https://ift.tt/7othqR4 [3] https://www.youtube.com/watch?v=zrv5Cy1R7r4
28 by bcantrill | 0 comments on Hacker News.
This RFD describes our distillation of a really gnarly issue that we hit in the Oxide control plane.[0] Not unlike our discovery of the async cancellation issue[1][2][3], this is larger than the issue itself -- and worse, the program that hits futurelock is correct from the programmer's point of view. Fortunately, the surface area here is smaller than that of async cancellation and the conditions required to hit it can be relatively easily mitigated. Still, this is a pretty deep issue -- and something that took some very seasoned Rust hands quite a while to find. [0] https://ift.tt/tOr7IlP [1] https://ift.tt/h3jp7iL [2] https://ift.tt/7othqR4 [3] https://www.youtube.com/watch?v=zrv5Cy1R7r4
Thursday, October 30, 2025
Wednesday, October 29, 2025
Tuesday, October 28, 2025
Monday, October 27, 2025
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Saturday, October 25, 2025
New top story on Hacker News: Dead soldiers' teeth reveal diseases that doomed Napoleon's army
Dead soldiers' teeth reveal diseases that doomed Napoleon's army
6 by reaperducer | 65 comments on Hacker News.
https://ift.tt/Th28JPs... https://ift.tt/izDV3NZ...
6 by reaperducer | 65 comments on Hacker News.
https://ift.tt/Th28JPs... https://ift.tt/izDV3NZ...
Friday, October 24, 2025
Thursday, October 23, 2025
New top story on Hacker News: Show HN: Tommy – Turn ESP32 devices into through-wall motion sensors
Show HN: Tommy – Turn ESP32 devices into through-wall motion sensors
7 by mike2872 | 1 comments on Hacker News.
Hi HN! I would like to present my project called TOMMY, which turns ESP32 devices into motion sensors that work through walls and obstacles using Wi-Fi sensing. TOMMY started as a project for my own use. I was frustrated with motion sensors that didn't detect stationary presence and left dead zones everywhere. Presence sensors existed but were expensive and needed one per room. I explored echo localization first, but microphones listening 24/7 felt too creepy. Then I discovered Wi-Fi sensing - a huge research topic but nothing production-ready yet. It ticked all the boxes: could theoretically detect stationary presence through breathing/micromovements and worked through walls and furniture so devices could be hidden away. Two years and dozens of research papers later, TOMMY has evolved into software I'm honestly quite proud of. Although it doesn't have stationary presence detection yet (coming Q1 2026) it detects motion really well. It works as a Home Assistant Add-on or Docker container, supports a range of ESP32 devices, and can be flashed through the built-in tool or used alongside existing ESPHome setups. I released the first version a couple of months ago on Home Assistant's subreddit and got a lot of interest and positive feedback. More than 200 people joined the Discord community and almost 2,000 downloaded it. Right now TOMMY is in beta, which is completely free for everyone to use. I'm also offering free lifetime licenses to every beta user who joins the Discord channel. You can read more about the project on https://ift.tt/hgiKJyr . Please join the Discord channel if you are interested in the project. A note on open source: There's been a lot of interest in having TOMMY as an open source project, which I fully understand. I'm reluctant to open source before reaching sustainability, as I'd love to work on this full time. However, privacy is verifiable - it's 100% local with no data collection (easily confirmed via packet sniffing or network isolation). Happy to help anyone verify this.
7 by mike2872 | 1 comments on Hacker News.
Hi HN! I would like to present my project called TOMMY, which turns ESP32 devices into motion sensors that work through walls and obstacles using Wi-Fi sensing. TOMMY started as a project for my own use. I was frustrated with motion sensors that didn't detect stationary presence and left dead zones everywhere. Presence sensors existed but were expensive and needed one per room. I explored echo localization first, but microphones listening 24/7 felt too creepy. Then I discovered Wi-Fi sensing - a huge research topic but nothing production-ready yet. It ticked all the boxes: could theoretically detect stationary presence through breathing/micromovements and worked through walls and furniture so devices could be hidden away. Two years and dozens of research papers later, TOMMY has evolved into software I'm honestly quite proud of. Although it doesn't have stationary presence detection yet (coming Q1 2026) it detects motion really well. It works as a Home Assistant Add-on or Docker container, supports a range of ESP32 devices, and can be flashed through the built-in tool or used alongside existing ESPHome setups. I released the first version a couple of months ago on Home Assistant's subreddit and got a lot of interest and positive feedback. More than 200 people joined the Discord community and almost 2,000 downloaded it. Right now TOMMY is in beta, which is completely free for everyone to use. I'm also offering free lifetime licenses to every beta user who joins the Discord channel. You can read more about the project on https://ift.tt/hgiKJyr . Please join the Discord channel if you are interested in the project. A note on open source: There's been a lot of interest in having TOMMY as an open source project, which I fully understand. I'm reluctant to open source before reaching sustainability, as I'd love to work on this full time. However, privacy is verifiable - it's 100% local with no data collection (easily confirmed via packet sniffing or network isolation). Happy to help anyone verify this.
Wednesday, October 22, 2025
Tuesday, October 21, 2025
Monday, October 20, 2025
Sunday, October 19, 2025
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Friday, October 17, 2025
New top story on Hacker News: Show HN: We packaged an MCP server inside Chromium
Show HN: We packaged an MCP server inside Chromium
8 by felarof | 5 comments on Hacker News.
Hey HN, we just shipped a browser with an inbuilt MCP server! We're a YC startup (S24) building BrowserOS — an open‑source Chromium fork. We're a privacy‑first alternative to the new wave of AI browsers like Dia, Perplexity Comet. Since launching ~3 months ago, the #1 request has been to expose our browser as an MCP server. -- Google beat us to launch with chrome-devtools-mcp (solid product btw), which lets you build/debug web apps by connecting Chrome to coding assistants. But we wanted to take this a step further: we packaged the MCP server directly into our browser binary. That gives three advantages: 1. MCP server setup is super simple — no npx install, no starting Chrome with CDP flags, you just download the BrowserOS binary. 2. with our browser's inbuilt MCP server, AI agents can interact using your logged‑in sessions (unlike chrome-devtools-mcp which starts a fresh headless instance each time) 3. our MCP server also exposes new APIs from Chromium's C++ core to click, type, and draw bounding boxes on a webpage. Our APIs are also not CDP-based (Chrome Debug Protocol) and have robust anti-bot detection. -- Few example use cases for BrowserOS-mcp are: a) *Frontend development with Claude Code*: instead of screenshot‑pasting, claude-code gets WYSIWYG access. It can write code, take a screenshot, check console logs, and fix issues in one agentic sweep. Since it has your sessions, it can do QA stuff like "test the auth flow with my Google Sign‑In." Here's a video of claude-code using browserOS to improve the css styling with back-and-forth checking: https://youtu.be/vcSxzIIkg_0 b) *Use as an agentic browser:* You can install BrowserOS-mcp in claude-code or Claude Desktop and do things like form-filling, extraction, multi-step agentic tasks, etc. It honestly works better than Perplexity Comet! Here's a video of claude-code opening top 5 hacker news posts and summarizing: https://youtu.be/rPFx_Btajj0 -- *How we packaged MCP server inside Chromium binary*: We package the server as a Bun binary and expose MCP tools over HTTP instead of stdio (to support multiple sessions). And we have a BrowserOS controller installed as an extension at the application layer which the MCP server connects to over WebSocket to control the browser. Here's a rough architecture diagram: https://dub.sh/browseros-mcp-diag -- *How to install and use it:* We put together a short guide here: https://ift.tt/59c8yhG Our vision is to reimagine the browser as an operating system for AI agents, and packaging an MCP server directly into it is a big unlock for that! I'll be hanging around all day, would love to get your feedback and answer any questions!
8 by felarof | 5 comments on Hacker News.
Hey HN, we just shipped a browser with an inbuilt MCP server! We're a YC startup (S24) building BrowserOS — an open‑source Chromium fork. We're a privacy‑first alternative to the new wave of AI browsers like Dia, Perplexity Comet. Since launching ~3 months ago, the #1 request has been to expose our browser as an MCP server. -- Google beat us to launch with chrome-devtools-mcp (solid product btw), which lets you build/debug web apps by connecting Chrome to coding assistants. But we wanted to take this a step further: we packaged the MCP server directly into our browser binary. That gives three advantages: 1. MCP server setup is super simple — no npx install, no starting Chrome with CDP flags, you just download the BrowserOS binary. 2. with our browser's inbuilt MCP server, AI agents can interact using your logged‑in sessions (unlike chrome-devtools-mcp which starts a fresh headless instance each time) 3. our MCP server also exposes new APIs from Chromium's C++ core to click, type, and draw bounding boxes on a webpage. Our APIs are also not CDP-based (Chrome Debug Protocol) and have robust anti-bot detection. -- Few example use cases for BrowserOS-mcp are: a) *Frontend development with Claude Code*: instead of screenshot‑pasting, claude-code gets WYSIWYG access. It can write code, take a screenshot, check console logs, and fix issues in one agentic sweep. Since it has your sessions, it can do QA stuff like "test the auth flow with my Google Sign‑In." Here's a video of claude-code using browserOS to improve the css styling with back-and-forth checking: https://youtu.be/vcSxzIIkg_0 b) *Use as an agentic browser:* You can install BrowserOS-mcp in claude-code or Claude Desktop and do things like form-filling, extraction, multi-step agentic tasks, etc. It honestly works better than Perplexity Comet! Here's a video of claude-code opening top 5 hacker news posts and summarizing: https://youtu.be/rPFx_Btajj0 -- *How we packaged MCP server inside Chromium binary*: We package the server as a Bun binary and expose MCP tools over HTTP instead of stdio (to support multiple sessions). And we have a BrowserOS controller installed as an extension at the application layer which the MCP server connects to over WebSocket to control the browser. Here's a rough architecture diagram: https://dub.sh/browseros-mcp-diag -- *How to install and use it:* We put together a short guide here: https://ift.tt/59c8yhG Our vision is to reimagine the browser as an operating system for AI agents, and packaging an MCP server directly into it is a big unlock for that! I'll be hanging around all day, would love to get your feedback and answer any questions!
Thursday, October 16, 2025
Wednesday, October 15, 2025
Tuesday, October 14, 2025
New top story on Hacker News: What do Americans die from vs. what the news report on
What do Americans die from vs. what the news report on
16 by alphabetatango | 5 comments on Hacker News.
16 by alphabetatango | 5 comments on Hacker News.
Monday, October 13, 2025
Sunday, October 12, 2025
New top story on Hacker News: Show HN: I built a simple ambient sound app with no ads or subscriptions
Show HN: I built a simple ambient sound app with no ads or subscriptions
9 by alpaca121 | 2 comments on Hacker News.
I’ve always liked having background noise while working or falling asleep, but I got frustrated that most “white noise” or ambient sound apps are either paywalled, stuffed with ads, or try to upsell subscriptions for basic features. So I made Ambi, a small iOS app with a clean interface and a set of freely available ambient sounds — rain, waves, wind, birds, that sort of thing. You can mix them, adjust volume levels, and just let it play all night or while you work. Everything works offline and there are no hidden catches. It’s something I built for myself first, but I figured others might find it useful too. Feedback, bugs, and suggestions are all welcome. https://ift.tt/TVhNL4C...
9 by alpaca121 | 2 comments on Hacker News.
I’ve always liked having background noise while working or falling asleep, but I got frustrated that most “white noise” or ambient sound apps are either paywalled, stuffed with ads, or try to upsell subscriptions for basic features. So I made Ambi, a small iOS app with a clean interface and a set of freely available ambient sounds — rain, waves, wind, birds, that sort of thing. You can mix them, adjust volume levels, and just let it play all night or while you work. Everything works offline and there are no hidden catches. It’s something I built for myself first, but I figured others might find it useful too. Feedback, bugs, and suggestions are all welcome. https://ift.tt/TVhNL4C...
Saturday, October 11, 2025
Friday, October 10, 2025
Thursday, October 9, 2025
Wednesday, October 8, 2025
New top story on Hacker News: Show HN: I built a local-first podcast app
Show HN: I built a local-first podcast app
13 by aegrumet | 4 comments on Hacker News.
I worked on early podcast software in 2004 (iPodder/Juice) and have been a heavy podcast consumer ever since. I wanted a podcast app that respects your privacy and embraces the open web—and to explore what's possible in the browser. The result is wherever.audio, which you can try right now at the link above. How it works: It's a progressive web app that stores all your subscriptions and data locally in your browser using IndexedDB. Add it to your home screen and it feels native. Works offline with downloaded episodes. No central server storing your data—just some Cloudflare/AWS helpers to smooth out browser limitations. What makes it different: - True local-first: Your data stays on your device - Custom feeds: Add any RSS feed, not just what's in a directory - On-device search: Search across all feeds and episodes, including your custom ones - Podcasting 2.0 support: Chapters, transcripts, funding tags, and others - Auto-generated chapters: For popular shows that don't have them - AI-powered discovery: Ask questions to find shows and episodes (this feature does send queries to a 3rd party API, and also uses anonymized analytics while we work out the prompts) - Audio-guided tutorials: Interactive walkthroughs with voice guidance and visual cues The basics work well too: Standard playback features, queue management, speed controls, etc. I'm really interested in feedback—this is more passion project than business right now. I've been dogfooding it as my daily podcast app for over a year, and I'm open to exploring making it a business if people find it valuable. Curious if there are unmet needs that a privacy-focused, open web approach could address.
13 by aegrumet | 4 comments on Hacker News.
I worked on early podcast software in 2004 (iPodder/Juice) and have been a heavy podcast consumer ever since. I wanted a podcast app that respects your privacy and embraces the open web—and to explore what's possible in the browser. The result is wherever.audio, which you can try right now at the link above. How it works: It's a progressive web app that stores all your subscriptions and data locally in your browser using IndexedDB. Add it to your home screen and it feels native. Works offline with downloaded episodes. No central server storing your data—just some Cloudflare/AWS helpers to smooth out browser limitations. What makes it different: - True local-first: Your data stays on your device - Custom feeds: Add any RSS feed, not just what's in a directory - On-device search: Search across all feeds and episodes, including your custom ones - Podcasting 2.0 support: Chapters, transcripts, funding tags, and others - Auto-generated chapters: For popular shows that don't have them - AI-powered discovery: Ask questions to find shows and episodes (this feature does send queries to a 3rd party API, and also uses anonymized analytics while we work out the prompts) - Audio-guided tutorials: Interactive walkthroughs with voice guidance and visual cues The basics work well too: Standard playback features, queue management, speed controls, etc. I'm really interested in feedback—this is more passion project than business right now. I've been dogfooding it as my daily podcast app for over a year, and I'm open to exploring making it a business if people find it valuable. Curious if there are unmet needs that a privacy-focused, open web approach could address.
Tuesday, October 7, 2025
Monday, October 6, 2025
Sunday, October 5, 2025
New top story on Hacker News: Show HN: ut – Rust based CLI utilities for devs and IT
Show HN: ut – Rust based CLI utilities for devs and IT
12 by ksdme9 | 5 comments on Hacker News.
Hey HN, I find myself reaching for tools like it-tools.tech or other random sites every now and then during development or debugging. So, I built a toolkit with a sane and simple CLI interface for most of those tools. For the curious and lazy, at the moment, ut has tools for, - Encoding: base64 (encode, decode), url (encode, decode) - Hashing: md5, sha1, sha224, sha256, sha384, sha512 - Data Generation: uuid (v1, v3, v4, v5), token, lorem, random - Text Processing: case (lower, upper, camel, title, constant, header, sentence, snake), pretty-print, diff - Development Tools: calc, json (builder), regex, datetime - Web & Network: http (status), serve, qr - Color & Design: color (convert) - Reference: unicode For full disclosure, parts of the toolkit were built with Claude Code (I wanted to use this as an opportunity to play with it more). Feel free to open feature requests and/or contribute.
12 by ksdme9 | 5 comments on Hacker News.
Hey HN, I find myself reaching for tools like it-tools.tech or other random sites every now and then during development or debugging. So, I built a toolkit with a sane and simple CLI interface for most of those tools. For the curious and lazy, at the moment, ut has tools for, - Encoding: base64 (encode, decode), url (encode, decode) - Hashing: md5, sha1, sha224, sha256, sha384, sha512 - Data Generation: uuid (v1, v3, v4, v5), token, lorem, random - Text Processing: case (lower, upper, camel, title, constant, header, sentence, snake), pretty-print, diff - Development Tools: calc, json (builder), regex, datetime - Web & Network: http (status), serve, qr - Color & Design: color (convert) - Reference: unicode For full disclosure, parts of the toolkit were built with Claude Code (I wanted to use this as an opportunity to play with it more). Feel free to open feature requests and/or contribute.
Saturday, October 4, 2025
Friday, October 3, 2025
Thursday, October 2, 2025
Wednesday, October 1, 2025
Tuesday, September 30, 2025
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Tuesday, September 23, 2025
New top story on Hacker News: Show HN: FlyCode – Recover Stripe payments by automatically using backup cards
Show HN: FlyCode – Recover Stripe payments by automatically using backup cards
11 by JakeVacovec | 26 comments on Hacker News.
We built FlyCode after seeing subscription businesses lose ~35% of recurring revenue each year to failed payments — even when customers had other valid cards on file. *The problem:* When a customer's primary card fails, Stripe retries a few times then cancels the subscription. If that customer has a backup card, it isn’t tried. At least 20% of active customers have more than one card on file, which means a lot of preventable churn. *Our solution:* FlyCode automatically identifies if a customer has other valid cards on file and retries them when a subscription payment fails. You can configure when these retries happen during the dunning period (beginning, middle, end) and define validity rules (e.g. “card was used in last 180 days”). It’s a Stripe app — no code changes needed. We've seen 18%-20% higher recovery rates from our core retry engine, plus another 5–10% from using backup cards. Importantly, there was no increase in refunds or chargebacks — in fact, rates were lower than merchant averages. Big companies like Microsoft and Amazon already do this internally; we wanted to make the same capability accessible to smaller SaaS teams. *Under the hood:* FlyCode monitors for failed invoices, checks for available backup methods via Stripe’s PaymentMethod API, and systematically retries in a way that avoids service disruption or manual workflows. We’re Jake, Etai, and Tzachi — we previously built payment recovery systems at startups and enterprises, which is how we discovered this gap. You can try it here: [ https://ift.tt/HQWejr3 ] We’d love feedback from anyone dealing with subscription payment failures. What’s been your experience with involuntary churn? Have you considered leveraging backup payment methods?
11 by JakeVacovec | 26 comments on Hacker News.
We built FlyCode after seeing subscription businesses lose ~35% of recurring revenue each year to failed payments — even when customers had other valid cards on file. *The problem:* When a customer's primary card fails, Stripe retries a few times then cancels the subscription. If that customer has a backup card, it isn’t tried. At least 20% of active customers have more than one card on file, which means a lot of preventable churn. *Our solution:* FlyCode automatically identifies if a customer has other valid cards on file and retries them when a subscription payment fails. You can configure when these retries happen during the dunning period (beginning, middle, end) and define validity rules (e.g. “card was used in last 180 days”). It’s a Stripe app — no code changes needed. We've seen 18%-20% higher recovery rates from our core retry engine, plus another 5–10% from using backup cards. Importantly, there was no increase in refunds or chargebacks — in fact, rates were lower than merchant averages. Big companies like Microsoft and Amazon already do this internally; we wanted to make the same capability accessible to smaller SaaS teams. *Under the hood:* FlyCode monitors for failed invoices, checks for available backup methods via Stripe’s PaymentMethod API, and systematically retries in a way that avoids service disruption or manual workflows. We’re Jake, Etai, and Tzachi — we previously built payment recovery systems at startups and enterprises, which is how we discovered this gap. You can try it here: [ https://ift.tt/HQWejr3 ] We’d love feedback from anyone dealing with subscription payment failures. What’s been your experience with involuntary churn? Have you considered leveraging backup payment methods?
Monday, September 22, 2025
New top story on Hacker News: Qwen3-Omni: Native Omni AI Model for Text, Image & Video
Qwen3-Omni: Native Omni AI Model for Text, Image & Video
19 by meetpateltech | 1 comments on Hacker News.
19 by meetpateltech | 1 comments on Hacker News.
Sunday, September 21, 2025
Saturday, September 20, 2025
Friday, September 19, 2025
Thursday, September 18, 2025
Wednesday, September 17, 2025
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Wednesday, September 10, 2025
New top story on Hacker News: Show HN: Haystack – Review pull requests like you wrote them yourself
Show HN: Haystack – Review pull requests like you wrote them yourself
21 by akshaysg | 7 comments on Hacker News.
Hi HN! We’re Akshay and Jake. We put together a tool called Haystack to make pull requests straightforward to read. What Haystack does: -- Builds a clear narrative. Changes in Haystack aren’t just arranged as unordered diffs. Instead, they unfold in a logical order, each paired with an explanation in plain, precise language -- Focuses attention where it counts. Routine plumbing and refactors are put into skimmable sections so you can spend your time on design and correctness -- Provides full cross-file context. Every new or changed function/variable is traced across the codebase, showing how it’s used beyond the immediate diff Here’s a quick demo: https://youtu.be/w5Lq5wBUS-I If you’d like to give it a spin, head over to haystackeditor.com/review! We set up some demo PRs that you should be able to understand and review even if you’ve never seen the repos before! We used to work at big companies, where reviewing non-trivial pull requests felt like reading a book with its pages out of order. We would jump and scroll between files, trying to piece together the author’s intent before we could even start reviewing. And, as authors, we would spend time to restructure our own commits just to make them readable. AI has made this even trickier. Today it’s not uncommon for a pull request to contain code the author doesn’t fully understand themselves! So, we built Haystack to help reviewers spend less time untangling code and more time giving meaningful feedback. We would love to hear about whether it gets the job done for you! How we got here: Haystack began as (yet another) VS Code fork where we experimented with visualizing code changes on a canvas. At first, it was a neat way to show how pieces of code worked together. But customers started laying out their entire codebase just to make sense of it. That’s when we realized the deeper problem: understanding a codebase is hard, and engineers need better ways to quickly understand unfamiliar code. As we kept building, another insight emerged: with AI woven into workflows, engineers don’t always need to master every corner of a codebase to ship features. But in code review, deep and continuous context still matters, especially to separate what’s important to review from plumbing and follow-on changes. So we pivoted. We took what we’d learned and worked closely with engineers to refine the idea. We started with simple code analysis (using language servers, tree-sitter, etc.) to show how changes relate. Then we added AI to explain and organize those changes and to trace how data moves through a pull request. Finally, we fused the two by empowering AI agents to use static analyses. Step by step, that became the Haystack we’re showing today. We’d love to hear your thoughts, feedback, or suggestions!
21 by akshaysg | 7 comments on Hacker News.
Hi HN! We’re Akshay and Jake. We put together a tool called Haystack to make pull requests straightforward to read. What Haystack does: -- Builds a clear narrative. Changes in Haystack aren’t just arranged as unordered diffs. Instead, they unfold in a logical order, each paired with an explanation in plain, precise language -- Focuses attention where it counts. Routine plumbing and refactors are put into skimmable sections so you can spend your time on design and correctness -- Provides full cross-file context. Every new or changed function/variable is traced across the codebase, showing how it’s used beyond the immediate diff Here’s a quick demo: https://youtu.be/w5Lq5wBUS-I If you’d like to give it a spin, head over to haystackeditor.com/review! We set up some demo PRs that you should be able to understand and review even if you’ve never seen the repos before! We used to work at big companies, where reviewing non-trivial pull requests felt like reading a book with its pages out of order. We would jump and scroll between files, trying to piece together the author’s intent before we could even start reviewing. And, as authors, we would spend time to restructure our own commits just to make them readable. AI has made this even trickier. Today it’s not uncommon for a pull request to contain code the author doesn’t fully understand themselves! So, we built Haystack to help reviewers spend less time untangling code and more time giving meaningful feedback. We would love to hear about whether it gets the job done for you! How we got here: Haystack began as (yet another) VS Code fork where we experimented with visualizing code changes on a canvas. At first, it was a neat way to show how pieces of code worked together. But customers started laying out their entire codebase just to make sense of it. That’s when we realized the deeper problem: understanding a codebase is hard, and engineers need better ways to quickly understand unfamiliar code. As we kept building, another insight emerged: with AI woven into workflows, engineers don’t always need to master every corner of a codebase to ship features. But in code review, deep and continuous context still matters, especially to separate what’s important to review from plumbing and follow-on changes. So we pivoted. We took what we’d learned and worked closely with engineers to refine the idea. We started with simple code analysis (using language servers, tree-sitter, etc.) to show how changes relate. Then we added AI to explain and organize those changes and to trace how data moves through a pull request. Finally, we fused the two by empowering AI agents to use static analyses. Step by step, that became the Haystack we’re showing today. We’d love to hear your thoughts, feedback, or suggestions!
New top story on Hacker News: Launch HN: Recall.ai (YC W20) – API for meeting recordings and transcripts
Launch HN: Recall.ai (YC W20) – API for meeting recordings and transcripts
18 by davidgu | 8 comments on Hacker News.
Hey HN, we're David and Amanda from Recall.ai ( https://www.recall.ai ). Today we’re launching our Desktop Recording SDK, a way to get meeting data without a bot in the meeting: https://ift.tt/Ha1oxML . It’s our biggest release in quite a while so we thought we’d finally do our Launch HN :) Here’s a demo that shows it producing a transcript from a meeting, followed by examples in code: https://www.youtube.com/watch?v=4croAGGiKTA . API docs are at https://docs.recall.ai/ . Back in W20, our first product was an API that lets you send a bot participant into a meeting. This gives developers access to audio/video streams and other data in the meeting. Today, this API powers most of the meeting recording products on the market. Recently, meeting recording through a desktop form factor instead of a bot has become popular. Many products like Notion and ChatGPT have added desktop recording functionality, and LLMs have made it easier to work with unstructured transcripts. But it’s actually hard to reliably record meetings at scale with a desktop app, and most developers who want to add recording functionality don’t want to build all this infrastructure. Doing a basic recording with just the microphone and system audio is fairly straightforward since you can just use the system APIs. But it gets a lot harder when you want to capture speaker names, produce a video recording, get real-time data, or run this in production at large scale: - Capturing speaker names involves using accessibility APIs to screen-scrape the video conference window to monitor who is speaking at what time. When video conferencing platforms change their UI, we must ship a change immediately, so this keeps working. - Producing a video recording that is clean, and doesn’t capture the video conferencing platform UI involves detecting the participant tiles, cropping them out, and compositing them together into a clean video recording. - Because the desktop recording code runs on end-user machines, we need to make it as efficient as possible. This means writing highly platform-optimized code, taking advantage of hardware encoders when available, and spending a lot of time doing profiling and performance testing. Meeting recording has zero margin for failure because if anything breaks, you lose the data forever. Reliability is especially important, which dramatically increases the amount of engineering effort required. Our Desktop Recording SDK takes care of all this and lets developers build meeting recording features into their desktop apps, so they can record both video conferences and in-person meetings without a bot. We built Recall.ai because we experienced this problem ourselves. At our first startup, we built a tool for product managers that included a meeting recording feature. 70% of our engineering time was taken up by just this feature! We ended up starting Recall.ai to solve this instead. Since then, over 2000 companies use us to power their recording features, e.g. Hubspot for sales call recording, Clickup for their AI note taker. Our users are engineering teams building commercial products for financial services, telehealth, incident management, sales, interviewing, and more. We also power internal tooling for large enterprises. Running this sort of infrastructure has led to unexpected technical challenges! For example, we had to debug a 1 in 36 million segfault in our audio encoder ( https://ift.tt/oxurgyG... ), we encountered a Postgres lock-up that only occurs when you have tens of thousands of concurrent writers ( https://ift.tt/2T3nEPH ), and we saved over $1M a year on AWS by optimizing the way we shuffle data around between our processes ( https://ift.tt/T0zmP6R ). You can try it here: https://www.recall.ai . It's self-serve with $5 of free credits. Pricing starts at $0.70 for every hour of recording, prorated to the second. We offer volume discounts with scale. All data recorded through Recall.ai is the property of our customers, we support 0-day retention, and we don’t train models on customer data. We would love your feedback!
18 by davidgu | 8 comments on Hacker News.
Hey HN, we're David and Amanda from Recall.ai ( https://www.recall.ai ). Today we’re launching our Desktop Recording SDK, a way to get meeting data without a bot in the meeting: https://ift.tt/Ha1oxML . It’s our biggest release in quite a while so we thought we’d finally do our Launch HN :) Here’s a demo that shows it producing a transcript from a meeting, followed by examples in code: https://www.youtube.com/watch?v=4croAGGiKTA . API docs are at https://docs.recall.ai/ . Back in W20, our first product was an API that lets you send a bot participant into a meeting. This gives developers access to audio/video streams and other data in the meeting. Today, this API powers most of the meeting recording products on the market. Recently, meeting recording through a desktop form factor instead of a bot has become popular. Many products like Notion and ChatGPT have added desktop recording functionality, and LLMs have made it easier to work with unstructured transcripts. But it’s actually hard to reliably record meetings at scale with a desktop app, and most developers who want to add recording functionality don’t want to build all this infrastructure. Doing a basic recording with just the microphone and system audio is fairly straightforward since you can just use the system APIs. But it gets a lot harder when you want to capture speaker names, produce a video recording, get real-time data, or run this in production at large scale: - Capturing speaker names involves using accessibility APIs to screen-scrape the video conference window to monitor who is speaking at what time. When video conferencing platforms change their UI, we must ship a change immediately, so this keeps working. - Producing a video recording that is clean, and doesn’t capture the video conferencing platform UI involves detecting the participant tiles, cropping them out, and compositing them together into a clean video recording. - Because the desktop recording code runs on end-user machines, we need to make it as efficient as possible. This means writing highly platform-optimized code, taking advantage of hardware encoders when available, and spending a lot of time doing profiling and performance testing. Meeting recording has zero margin for failure because if anything breaks, you lose the data forever. Reliability is especially important, which dramatically increases the amount of engineering effort required. Our Desktop Recording SDK takes care of all this and lets developers build meeting recording features into their desktop apps, so they can record both video conferences and in-person meetings without a bot. We built Recall.ai because we experienced this problem ourselves. At our first startup, we built a tool for product managers that included a meeting recording feature. 70% of our engineering time was taken up by just this feature! We ended up starting Recall.ai to solve this instead. Since then, over 2000 companies use us to power their recording features, e.g. Hubspot for sales call recording, Clickup for their AI note taker. Our users are engineering teams building commercial products for financial services, telehealth, incident management, sales, interviewing, and more. We also power internal tooling for large enterprises. Running this sort of infrastructure has led to unexpected technical challenges! For example, we had to debug a 1 in 36 million segfault in our audio encoder ( https://ift.tt/oxurgyG... ), we encountered a Postgres lock-up that only occurs when you have tens of thousands of concurrent writers ( https://ift.tt/2T3nEPH ), and we saved over $1M a year on AWS by optimizing the way we shuffle data around between our processes ( https://ift.tt/T0zmP6R ). You can try it here: https://www.recall.ai . It's self-serve with $5 of free credits. Pricing starts at $0.70 for every hour of recording, prorated to the second. We offer volume discounts with scale. All data recorded through Recall.ai is the property of our customers, we support 0-day retention, and we don’t train models on customer data. We would love your feedback!
Tuesday, September 9, 2025
Monday, September 8, 2025
Sunday, September 7, 2025
Saturday, September 6, 2025
Friday, September 5, 2025
New top story on Hacker News: Show HN: Open-sourcing our text-to-CAD app
Show HN: Open-sourcing our text-to-CAD app
23 by zachdive | 4 comments on Hacker News.
Hey HN! I'm Zach from Adam ( https://adam.new/ ). We’re building an AI co-pilot for mechanical CAD software. As part of our broader research, we built a browser-based Text-to-CAD app ( https://ift.tt/RF8YrJx ) and are now open sourcing it. This is a React SPA with a Supabase backend. What it does: * Generates parametric 3D models from natural language descriptions, with support for both text prompts and image references * Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking * Exports as .STL or .SCAD Under the hood: * Separate agents for conversation and code generation; simple parameter tweaks bypass AI entirely using deterministic regex-based updates * Runs fully in-browser by compiling OpenSCAD to WebAssembly and integrating Three.js with React Three Fiber for 3D rendering * Supports BOSL, BOSL2, MCAD libraries and custom font support (Geist) for text in models We’ve seen many developers trying to replicate this kind of functionality, so we’re releasing this to give the community a solid foundation to build on. Future improvements: * Expand geometry support - Move beyond CSG primitives to support curved surfaces, fillets, lofts, and constraint-driven modeling through CadQuery/Build123D * Better spatial context - UI for face/edge selection and viewport image integration to give LLMs spatial understanding * Enhanced capabilities - RAG on documentation and integration with more OpenSCAD libraries for features like proper threading You can clone the repo and run it locally! Contributions are welcome, and we’ll keep merging PRs as they come in.
23 by zachdive | 4 comments on Hacker News.
Hey HN! I'm Zach from Adam ( https://adam.new/ ). We’re building an AI co-pilot for mechanical CAD software. As part of our broader research, we built a browser-based Text-to-CAD app ( https://ift.tt/RF8YrJx ) and are now open sourcing it. This is a React SPA with a Supabase backend. What it does: * Generates parametric 3D models from natural language descriptions, with support for both text prompts and image references * Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking * Exports as .STL or .SCAD Under the hood: * Separate agents for conversation and code generation; simple parameter tweaks bypass AI entirely using deterministic regex-based updates * Runs fully in-browser by compiling OpenSCAD to WebAssembly and integrating Three.js with React Three Fiber for 3D rendering * Supports BOSL, BOSL2, MCAD libraries and custom font support (Geist) for text in models We’ve seen many developers trying to replicate this kind of functionality, so we’re releasing this to give the community a solid foundation to build on. Future improvements: * Expand geometry support - Move beyond CSG primitives to support curved surfaces, fillets, lofts, and constraint-driven modeling through CadQuery/Build123D * Better spatial context - UI for face/edge selection and viewport image integration to give LLMs spatial understanding * Enhanced capabilities - RAG on documentation and integration with more OpenSCAD libraries for features like proper threading You can clone the repo and run it locally! Contributions are welcome, and we’ll keep merging PRs as they come in.
Thursday, September 4, 2025
New top story on Hacker News: A high schooler writes about AI tools in the classroom
A high schooler writes about AI tools in the classroom
91 by dougb5 | 79 comments on Hacker News.
https://ift.tt/DyLJXeW
91 by dougb5 | 79 comments on Hacker News.
https://ift.tt/DyLJXeW
Wednesday, September 3, 2025
New top story on Hacker News: Show HN: Entropy-Guided Loop – How to make small models reason
Show HN: Entropy-Guided Loop – How to make small models reason
13 by andrewmonostate | 0 comments on Hacker News.
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave like expensive black boxes. Meanwhile, standard inference already computes useful signals both before softmax normalization and after it(logprobs), which we usually throw away. This loop tries the simplest thing that you could think of: use those signals to decide when (and where) to think again. GitHub (notebook + minimal code): https://ift.tt/2h0qbFR Paper (short & engineer made): https://ift.tt/KLFgRd3 Blog (more context): https://ift.tt/70motVd Requirements: Python, API that exposes logprobs (tested with OpenAI non reasoning 4.1). OPENAI_API_KEY and WEAVE for observability. Run the notebook; it prints metrics and shows which tokens triggered refinement. - Python, simple loop (no retraining). - Uses Responses API logprobs/top-k; metrics: perplexity, max token entropy, low-confidence counts. - Weave for lightweight logging/observability (optional). - Passing alternatives (not just “this looks uncertain”) prevents over-correction. - A simple OR rule (ppl / max-entropy / low-confidence count) catches complementary failure modes. - Numbers drift across vendors; keeping the method vendor-agnostic is better than chasing fragile pairings. - Needs APIs that expose logprobs/top-k. - Results are indicative—not a leaderboard; focus is on within-model gains (single-pass vs +loop). - Thresholds might need light tuning per domain. - One pass only; not a chain-of-thought replacement. - Run it on your models and ideas (e.g., 4o-mini, v3, Llama variants with logprobs) and share logs in a PR for our README in GitHub if you'd like, PRs welcome - I’ll credit and link. Overall let me know if you find making small models reason like this useful!
13 by andrewmonostate | 0 comments on Hacker News.
TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave like expensive black boxes. Meanwhile, standard inference already computes useful signals both before softmax normalization and after it(logprobs), which we usually throw away. This loop tries the simplest thing that you could think of: use those signals to decide when (and where) to think again. GitHub (notebook + minimal code): https://ift.tt/2h0qbFR Paper (short & engineer made): https://ift.tt/KLFgRd3 Blog (more context): https://ift.tt/70motVd Requirements: Python, API that exposes logprobs (tested with OpenAI non reasoning 4.1). OPENAI_API_KEY and WEAVE for observability. Run the notebook; it prints metrics and shows which tokens triggered refinement. - Python, simple loop (no retraining). - Uses Responses API logprobs/top-k; metrics: perplexity, max token entropy, low-confidence counts. - Weave for lightweight logging/observability (optional). - Passing alternatives (not just “this looks uncertain”) prevents over-correction. - A simple OR rule (ppl / max-entropy / low-confidence count) catches complementary failure modes. - Numbers drift across vendors; keeping the method vendor-agnostic is better than chasing fragile pairings. - Needs APIs that expose logprobs/top-k. - Results are indicative—not a leaderboard; focus is on within-model gains (single-pass vs +loop). - Thresholds might need light tuning per domain. - One pass only; not a chain-of-thought replacement. - Run it on your models and ideas (e.g., 4o-mini, v3, Llama variants with logprobs) and share logs in a PR for our README in GitHub if you'd like, PRs welcome - I’ll credit and link. Overall let me know if you find making small models reason like this useful!
Tuesday, September 2, 2025
Monday, September 1, 2025
Sunday, August 31, 2025
New top story on Hacker News: Show HN: Anonymous Age Verification
Show HN: Anonymous Age Verification
6 by jwally | 2 comments on Hacker News.
So I'm not an expert in this area, but here's an attempt at cost effective, anonymous, age verification flow that probably covers ~70% of use cases in the United States. The basic premise is to leverage your bank (who already has had to perform KYC on you to open an account) to attest to your age for age-restricted merchant sites (pornhub, gambling, etc) without sharing any more information than necessary. Flow works like this: 1) You go to gambling.com 2) They request you to verify your age 3) You choose "Bank Verification" 4) You trigger a WebAuthn Credential Creation flow 5) gambling.com gives you a string to copy ------------- 6) You log into your bank 7) You go to bank.com/age-verify 8) You paste in the string you were given 9) The bank verifies it/you and creates a signed payload with your age-claims (over_18: true, over_21: false) 10) You copy this and go back to gambling.com --------------- 11) You paste the string back into gambling.com 12) You perform WebAuthn Auth flow 13) gambling.com verifies everything (signatures, webauthn, etc) 14) gambling.com sets a session-cookie and _STRONGLY_ encourages you to create an account (with a pass key). This will prevent you from having to verify your age every time you visit gambling.com The mechanics might feel off, but it feels like this in the neighborhood of a way to perform anonymous age verification. This is virtually free, and requires extremely light infra. Banks can be incentivized with small payments, or offer it because everyone else does and don't want to get left behind.
6 by jwally | 2 comments on Hacker News.
So I'm not an expert in this area, but here's an attempt at cost effective, anonymous, age verification flow that probably covers ~70% of use cases in the United States. The basic premise is to leverage your bank (who already has had to perform KYC on you to open an account) to attest to your age for age-restricted merchant sites (pornhub, gambling, etc) without sharing any more information than necessary. Flow works like this: 1) You go to gambling.com 2) They request you to verify your age 3) You choose "Bank Verification" 4) You trigger a WebAuthn Credential Creation flow 5) gambling.com gives you a string to copy ------------- 6) You log into your bank 7) You go to bank.com/age-verify 8) You paste in the string you were given 9) The bank verifies it/you and creates a signed payload with your age-claims (over_18: true, over_21: false) 10) You copy this and go back to gambling.com --------------- 11) You paste the string back into gambling.com 12) You perform WebAuthn Auth flow 13) gambling.com verifies everything (signatures, webauthn, etc) 14) gambling.com sets a session-cookie and _STRONGLY_ encourages you to create an account (with a pass key). This will prevent you from having to verify your age every time you visit gambling.com The mechanics might feel off, but it feels like this in the neighborhood of a way to perform anonymous age verification. This is virtually free, and requires extremely light infra. Banks can be incentivized with small payments, or offer it because everyone else does and don't want to get left behind.
Saturday, August 30, 2025
Friday, August 29, 2025
Thursday, August 28, 2025
Wednesday, August 27, 2025
Tuesday, August 26, 2025
New top story on Hacker News: Show HN: SecretMemoryLocker – File Encryption Without Static Passwords
Show HN: SecretMemoryLocker – File Encryption Without Static Passwords
9 by YuriiDev | 0 comments on Hacker News.
I built SecretMemoryLocker ( https://ift.tt/NQZA78q ), a file encryption tool that generates keys dynamically from your answers to personal questions instead of using a static master password. This makes offline brute-force attacks much more difficult. Think of it as a password manager that meets mnemonic seed recovery, but without storing any sensitive keys on disk. Why? I kept losing master passwords and wanted a solution that wasn't tied to a single point of failure. I also wanted to create a "digital legacy" that my family could access only under specific conditions. The core principle is knowledge-based encryption: the key only exists in memory when you provide the correct answers. Status: * MVP is ready for Windows (.exe). * Linux and macOS support is planned. * UI is available in English, Spanish, and Ukrainian. Key Features: * No Static Secrets: No master password or seed phrase is ever stored. The key is reconstructed on the fly. * Knowledge-Based Key Generation: The final encryption key is derived from a combination of your personal answers and file metadata. * Offline Brute-Force Resistance: Uses MirageLoop, a decoy system that activates when incorrect answers are entered. Instead of decrypting real data, it generates an endless sequence of AI-created questions from a secure local database, creating an illusion of progress while keeping your real data untouched. * Offline AI Generation Mode: Optional offline Q&A generator (prototype). How It Works (Simplified): 1) Files are packed into an AES-256 encrypted ZIP archive. 2) A JSON key file stores the questions in an encrypted chain. Each subsequent question is encrypted with a key derived from the previous correct answer and the file's hash. This forces you to answer them sequentially. 3) The final encryption key for the ZIP file is derived by combining the hashes of all your correct answers. The key derivation formula looks like this: K_final = SHA256(H(answer1+file_hash) + H(answer2+file_hash) + ...) (Note: We are aware that a fast hash like SHA256 is not ideal for a KDF. We plan to migrate to Argon2 in a future release to further strengthen resistance against brute-force attacks.) To encrypt, you provide a file. This creates two outputs: your_file.txt → your_file_SMLkey.json + your_file_SecretML.zip To decrypt, you need both files and the correct answers. Install & Quick Start: Download the EXE from GitHub Releases (no dependencies needed): https://ift.tt/xgK5jQk Encrypt: SecretMemoryLocker.exe --encrypt "C:\docs\important.pdf" Decrypt: SecretMemoryLocker.exe --decrypt "C:\docs\important_SMLkey.json" I would love to get your feedback on the concept, the user experience, and any security assumptions I've made. Thanks!
9 by YuriiDev | 0 comments on Hacker News.
I built SecretMemoryLocker ( https://ift.tt/NQZA78q ), a file encryption tool that generates keys dynamically from your answers to personal questions instead of using a static master password. This makes offline brute-force attacks much more difficult. Think of it as a password manager that meets mnemonic seed recovery, but without storing any sensitive keys on disk. Why? I kept losing master passwords and wanted a solution that wasn't tied to a single point of failure. I also wanted to create a "digital legacy" that my family could access only under specific conditions. The core principle is knowledge-based encryption: the key only exists in memory when you provide the correct answers. Status: * MVP is ready for Windows (.exe). * Linux and macOS support is planned. * UI is available in English, Spanish, and Ukrainian. Key Features: * No Static Secrets: No master password or seed phrase is ever stored. The key is reconstructed on the fly. * Knowledge-Based Key Generation: The final encryption key is derived from a combination of your personal answers and file metadata. * Offline Brute-Force Resistance: Uses MirageLoop, a decoy system that activates when incorrect answers are entered. Instead of decrypting real data, it generates an endless sequence of AI-created questions from a secure local database, creating an illusion of progress while keeping your real data untouched. * Offline AI Generation Mode: Optional offline Q&A generator (prototype). How It Works (Simplified): 1) Files are packed into an AES-256 encrypted ZIP archive. 2) A JSON key file stores the questions in an encrypted chain. Each subsequent question is encrypted with a key derived from the previous correct answer and the file's hash. This forces you to answer them sequentially. 3) The final encryption key for the ZIP file is derived by combining the hashes of all your correct answers. The key derivation formula looks like this: K_final = SHA256(H(answer1+file_hash) + H(answer2+file_hash) + ...) (Note: We are aware that a fast hash like SHA256 is not ideal for a KDF. We plan to migrate to Argon2 in a future release to further strengthen resistance against brute-force attacks.) To encrypt, you provide a file. This creates two outputs: your_file.txt → your_file_SMLkey.json + your_file_SecretML.zip To decrypt, you need both files and the correct answers. Install & Quick Start: Download the EXE from GitHub Releases (no dependencies needed): https://ift.tt/xgK5jQk Encrypt: SecretMemoryLocker.exe --encrypt "C:\docs\important.pdf" Decrypt: SecretMemoryLocker.exe --decrypt "C:\docs\important_SMLkey.json" I would love to get your feedback on the concept, the user experience, and any security assumptions I've made. Thanks!
Monday, August 25, 2025
Sunday, August 24, 2025
Saturday, August 23, 2025
Friday, August 22, 2025
Thursday, August 21, 2025
New top story on Hacker News: Show HN: Tool shows UK properties matching group commute/time preferences
Show HN: Tool shows UK properties matching group commute/time preferences
6 by fryingdan | 3 comments on Hacker News.
I came up with this idea when I was looking to move to London with a friend. I quickly learned how frustrating it is to trial-and-error housing options for days on end, just to be denied after days of searching due to some grotesque counteroffer. To add to this, finding properties that meet the budgets, commuting preferences and work locations of everyone in a group is a Sisyphean task - it often ends in failure, with somebody exceeding their original budget or somebody dropping out. To solve this I built a tool ( https://closemove.com/ ) that: - lets you enter between 1-6 people’s workplaces, budgets, and maximum commute times - filters public rental listings and only shows the ones that satisfy everyone’s constraints - shows results in either a list or map view No sign-up/validation required at present. Currently UK only, but please let me know if you'd want me to expand this to your city/country. This currently works best in London (with walking, cycling, driving and public transport links connected), and works decently in the rest of the UK (walking, cycling, driving only). This started as a side project and it still needs improvement. I’d appreciate any feedback!
6 by fryingdan | 3 comments on Hacker News.
I came up with this idea when I was looking to move to London with a friend. I quickly learned how frustrating it is to trial-and-error housing options for days on end, just to be denied after days of searching due to some grotesque counteroffer. To add to this, finding properties that meet the budgets, commuting preferences and work locations of everyone in a group is a Sisyphean task - it often ends in failure, with somebody exceeding their original budget or somebody dropping out. To solve this I built a tool ( https://closemove.com/ ) that: - lets you enter between 1-6 people’s workplaces, budgets, and maximum commute times - filters public rental listings and only shows the ones that satisfy everyone’s constraints - shows results in either a list or map view No sign-up/validation required at present. Currently UK only, but please let me know if you'd want me to expand this to your city/country. This currently works best in London (with walking, cycling, driving and public transport links connected), and works decently in the rest of the UK (walking, cycling, driving only). This started as a side project and it still needs improvement. I’d appreciate any feedback!
Wednesday, August 20, 2025
Tuesday, August 19, 2025
Monday, August 18, 2025
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