The best Hacker News stories from Show from the past day
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Show HN: We Put Chromium on a Unikernel (OSS Apache 2.0)
We’ve been building infrastructure to spin up browsers for AI agents. Originally, we built[0] it as a pool of warm Docker containers running Chromium, exposing:<p>- Chrome DevTools Protocol (for Playwright/Puppeteer)<p>- noVNC (for live view)<p>We’ve been following the unikernel space for a while, so we decided to see if we could get our image on one. We went with Unikraft Cloud[1]. Here’s how it did:<p>- Boot-up time: 10–20ms (vs. ~5s for Docker containers)<p>- Near 0 CPU/memory consumption when idle<p>- Still ~8GB RAM when active (headful Chromium)<p>Potential use cases:<p>- Standby mode during long-running jobs: unikernels can sleep after X sec of inactivity, reducing clock time costs<p>- Session reuse: auth/session cookies persist for hours/days. Basically as long as the cookies are valid<p>- Cold start speed: good for low-latency, event-based handling<p>We open sourced it with Apache 2.0! Feel free to fork or submit an issue / PR. Open to feedback or suggestions. www.github.com/onkernel/kernel-images<p>==<p>[0] <a href="https://github.com/onkernel/kernel-images">https://github.com/onkernel/kernel-images</a><p>[1] <a href="https://unikraft.cloud/" rel="nofollow">https://unikraft.cloud/</a><p>[2] Thanks to the Unikraft Cloud team @fhuici @nderjung @razvandeax for helping us figure this out (we're not affiliated)<p>[3] (OPs) @rgarcia @juecd
Show HN: We Put Chromium on a Unikernel (OSS Apache 2.0)
We’ve been building infrastructure to spin up browsers for AI agents. Originally, we built[0] it as a pool of warm Docker containers running Chromium, exposing:<p>- Chrome DevTools Protocol (for Playwright/Puppeteer)<p>- noVNC (for live view)<p>We’ve been following the unikernel space for a while, so we decided to see if we could get our image on one. We went with Unikraft Cloud[1]. Here’s how it did:<p>- Boot-up time: 10–20ms (vs. ~5s for Docker containers)<p>- Near 0 CPU/memory consumption when idle<p>- Still ~8GB RAM when active (headful Chromium)<p>Potential use cases:<p>- Standby mode during long-running jobs: unikernels can sleep after X sec of inactivity, reducing clock time costs<p>- Session reuse: auth/session cookies persist for hours/days. Basically as long as the cookies are valid<p>- Cold start speed: good for low-latency, event-based handling<p>We open sourced it with Apache 2.0! Feel free to fork or submit an issue / PR. Open to feedback or suggestions. www.github.com/onkernel/kernel-images<p>==<p>[0] <a href="https://github.com/onkernel/kernel-images">https://github.com/onkernel/kernel-images</a><p>[1] <a href="https://unikraft.cloud/" rel="nofollow">https://unikraft.cloud/</a><p>[2] Thanks to the Unikraft Cloud team @fhuici @nderjung @razvandeax for helping us figure this out (we're not affiliated)<p>[3] (OPs) @rgarcia @juecd
Show HN: We Put Chromium on a Unikernel (OSS Apache 2.0)
We’ve been building infrastructure to spin up browsers for AI agents. Originally, we built[0] it as a pool of warm Docker containers running Chromium, exposing:<p>- Chrome DevTools Protocol (for Playwright/Puppeteer)<p>- noVNC (for live view)<p>We’ve been following the unikernel space for a while, so we decided to see if we could get our image on one. We went with Unikraft Cloud[1]. Here’s how it did:<p>- Boot-up time: 10–20ms (vs. ~5s for Docker containers)<p>- Near 0 CPU/memory consumption when idle<p>- Still ~8GB RAM when active (headful Chromium)<p>Potential use cases:<p>- Standby mode during long-running jobs: unikernels can sleep after X sec of inactivity, reducing clock time costs<p>- Session reuse: auth/session cookies persist for hours/days. Basically as long as the cookies are valid<p>- Cold start speed: good for low-latency, event-based handling<p>We open sourced it with Apache 2.0! Feel free to fork or submit an issue / PR. Open to feedback or suggestions. www.github.com/onkernel/kernel-images<p>==<p>[0] <a href="https://github.com/onkernel/kernel-images">https://github.com/onkernel/kernel-images</a><p>[1] <a href="https://unikraft.cloud/" rel="nofollow">https://unikraft.cloud/</a><p>[2] Thanks to the Unikraft Cloud team @fhuici @nderjung @razvandeax for helping us figure this out (we're not affiliated)<p>[3] (OPs) @rgarcia @juecd
Show HN: Plandex v2 – open source AI coding agent for large projects and tasks
Hey HN! I’m Dane, the creator of Plandex (<a href="https://github.com/plandex-ai/plandex">https://github.com/plandex-ai/plandex</a>), an open source AI coding agent focused especially on tackling large tasks in real world software projects.<p>You can watch a 2 minute demo of Plandex in action here: <a href="https://www.youtube.com/watch?v=SFSu2vNmlLk" rel="nofollow">https://www.youtube.com/watch?v=SFSu2vNmlLk</a><p>And here’s more of a tutorial style demo showing how Plandex can automatically debug a browser application: <a href="https://www.youtube.com/watch?v=g-_76U_nK0Y" rel="nofollow">https://www.youtube.com/watch?v=g-_76U_nK0Y</a>.<p>I launched Plandex v1 here on HN a little less than a year ago (<a href="https://news.ycombinator.com/item?id=39918500">https://news.ycombinator.com/item?id=39918500</a>).<p>Now I’m launching a major update, Plandex v2, which is the result of 8 months of heads down work, and is in effect a whole new project/product.<p>In short, Plandex is now a top-tier coding agent with fully autonomous capabilities. It combines models from Anthropic, OpenAI, and Google to achieve better results, more reliable agent behavior, better cost efficiency, and better performance than is possible by using only a single provider’s models.<p>I believe it is now one of the best tools available for working on large tasks in real world codebases with AI. It has an effective context window of 2M tokens, and can index projects of 20M tokens and beyond using tree-sitter project maps (30+ languages are supported). It can effectively find relevant context in massive million-line projects like SQLite, Redis, and Git.<p>A bit more on some of Plandex’s key features:<p>- Plandex has a built-in diff review sandbox that helps you get the benefits of AI without leaving behind a mess in your project. By default, all changes accumulate in the sandbox until you approve them. The sandbox is version-controlled. You can rewind it to any previous point, and you can also create branches to try out alternative approaches.<p>- It offers a ‘full auto mode’ that can complete large tasks autonomously end-to-end, including high level planning, context loading, detailed planning, implementation, command execution (for dependencies, builds, tests, etc.), and debugging.<p>- The autonomy level is highly configurable. You can move up and down the ladder of autonomy depending on the task, your comfort level, and how you weigh cost optimization vs. effort and results.<p>- Models and model settings are also very configurable. There are built-in models and model packs for different use cases. You can also add custom models and model packs, and customize model settings like temperature or top-p. All model changes are version controlled, so you can use branches to try out the same task with different models. The newly released OpenAI models and the paid Gemini 2.5 Pro model will be integrated in the default model pack soon.<p>- It can be easily self-hosted, including a ‘local mode’ for a very fast local single-user setup with Docker.<p>- Cloud hosting is also available for added convenience with a couple of subscription tiers: an ‘Integrated Models’ mode that requires no other accounts or API keys and allows you to manage billing/budgeting/spending alerts and track usage centrally, and a ‘BYO API Key’ mode that allows you to use your own OpenAI/OpenRouter accounts.<p>I’d love to get more HNers in the Plandex Discord (<a href="https://discord.gg/plandex-ai" rel="nofollow">https://discord.gg/plandex-ai</a>). Please join and say hi!<p>And of course I’d love to hear your feedback, whether positive or negative. Thanks so much!
Show HN: Plandex v2 – open source AI coding agent for large projects and tasks
Hey HN! I’m Dane, the creator of Plandex (<a href="https://github.com/plandex-ai/plandex">https://github.com/plandex-ai/plandex</a>), an open source AI coding agent focused especially on tackling large tasks in real world software projects.<p>You can watch a 2 minute demo of Plandex in action here: <a href="https://www.youtube.com/watch?v=SFSu2vNmlLk" rel="nofollow">https://www.youtube.com/watch?v=SFSu2vNmlLk</a><p>And here’s more of a tutorial style demo showing how Plandex can automatically debug a browser application: <a href="https://www.youtube.com/watch?v=g-_76U_nK0Y" rel="nofollow">https://www.youtube.com/watch?v=g-_76U_nK0Y</a>.<p>I launched Plandex v1 here on HN a little less than a year ago (<a href="https://news.ycombinator.com/item?id=39918500">https://news.ycombinator.com/item?id=39918500</a>).<p>Now I’m launching a major update, Plandex v2, which is the result of 8 months of heads down work, and is in effect a whole new project/product.<p>In short, Plandex is now a top-tier coding agent with fully autonomous capabilities. It combines models from Anthropic, OpenAI, and Google to achieve better results, more reliable agent behavior, better cost efficiency, and better performance than is possible by using only a single provider’s models.<p>I believe it is now one of the best tools available for working on large tasks in real world codebases with AI. It has an effective context window of 2M tokens, and can index projects of 20M tokens and beyond using tree-sitter project maps (30+ languages are supported). It can effectively find relevant context in massive million-line projects like SQLite, Redis, and Git.<p>A bit more on some of Plandex’s key features:<p>- Plandex has a built-in diff review sandbox that helps you get the benefits of AI without leaving behind a mess in your project. By default, all changes accumulate in the sandbox until you approve them. The sandbox is version-controlled. You can rewind it to any previous point, and you can also create branches to try out alternative approaches.<p>- It offers a ‘full auto mode’ that can complete large tasks autonomously end-to-end, including high level planning, context loading, detailed planning, implementation, command execution (for dependencies, builds, tests, etc.), and debugging.<p>- The autonomy level is highly configurable. You can move up and down the ladder of autonomy depending on the task, your comfort level, and how you weigh cost optimization vs. effort and results.<p>- Models and model settings are also very configurable. There are built-in models and model packs for different use cases. You can also add custom models and model packs, and customize model settings like temperature or top-p. All model changes are version controlled, so you can use branches to try out the same task with different models. The newly released OpenAI models and the paid Gemini 2.5 Pro model will be integrated in the default model pack soon.<p>- It can be easily self-hosted, including a ‘local mode’ for a very fast local single-user setup with Docker.<p>- Cloud hosting is also available for added convenience with a couple of subscription tiers: an ‘Integrated Models’ mode that requires no other accounts or API keys and allows you to manage billing/budgeting/spending alerts and track usage centrally, and a ‘BYO API Key’ mode that allows you to use your own OpenAI/OpenRouter accounts.<p>I’d love to get more HNers in the Plandex Discord (<a href="https://discord.gg/plandex-ai" rel="nofollow">https://discord.gg/plandex-ai</a>). Please join and say hi!<p>And of course I’d love to hear your feedback, whether positive or negative. Thanks so much!
Show HN: Plandex v2 – open source AI coding agent for large projects and tasks
Hey HN! I’m Dane, the creator of Plandex (<a href="https://github.com/plandex-ai/plandex">https://github.com/plandex-ai/plandex</a>), an open source AI coding agent focused especially on tackling large tasks in real world software projects.<p>You can watch a 2 minute demo of Plandex in action here: <a href="https://www.youtube.com/watch?v=SFSu2vNmlLk" rel="nofollow">https://www.youtube.com/watch?v=SFSu2vNmlLk</a><p>And here’s more of a tutorial style demo showing how Plandex can automatically debug a browser application: <a href="https://www.youtube.com/watch?v=g-_76U_nK0Y" rel="nofollow">https://www.youtube.com/watch?v=g-_76U_nK0Y</a>.<p>I launched Plandex v1 here on HN a little less than a year ago (<a href="https://news.ycombinator.com/item?id=39918500">https://news.ycombinator.com/item?id=39918500</a>).<p>Now I’m launching a major update, Plandex v2, which is the result of 8 months of heads down work, and is in effect a whole new project/product.<p>In short, Plandex is now a top-tier coding agent with fully autonomous capabilities. It combines models from Anthropic, OpenAI, and Google to achieve better results, more reliable agent behavior, better cost efficiency, and better performance than is possible by using only a single provider’s models.<p>I believe it is now one of the best tools available for working on large tasks in real world codebases with AI. It has an effective context window of 2M tokens, and can index projects of 20M tokens and beyond using tree-sitter project maps (30+ languages are supported). It can effectively find relevant context in massive million-line projects like SQLite, Redis, and Git.<p>A bit more on some of Plandex’s key features:<p>- Plandex has a built-in diff review sandbox that helps you get the benefits of AI without leaving behind a mess in your project. By default, all changes accumulate in the sandbox until you approve them. The sandbox is version-controlled. You can rewind it to any previous point, and you can also create branches to try out alternative approaches.<p>- It offers a ‘full auto mode’ that can complete large tasks autonomously end-to-end, including high level planning, context loading, detailed planning, implementation, command execution (for dependencies, builds, tests, etc.), and debugging.<p>- The autonomy level is highly configurable. You can move up and down the ladder of autonomy depending on the task, your comfort level, and how you weigh cost optimization vs. effort and results.<p>- Models and model settings are also very configurable. There are built-in models and model packs for different use cases. You can also add custom models and model packs, and customize model settings like temperature or top-p. All model changes are version controlled, so you can use branches to try out the same task with different models. The newly released OpenAI models and the paid Gemini 2.5 Pro model will be integrated in the default model pack soon.<p>- It can be easily self-hosted, including a ‘local mode’ for a very fast local single-user setup with Docker.<p>- Cloud hosting is also available for added convenience with a couple of subscription tiers: an ‘Integrated Models’ mode that requires no other accounts or API keys and allows you to manage billing/budgeting/spending alerts and track usage centrally, and a ‘BYO API Key’ mode that allows you to use your own OpenAI/OpenRouter accounts.<p>I’d love to get more HNers in the Plandex Discord (<a href="https://discord.gg/plandex-ai" rel="nofollow">https://discord.gg/plandex-ai</a>). Please join and say hi!<p>And of course I’d love to hear your feedback, whether positive or negative. Thanks so much!
Show HN: Portable Giant File Viewer
I’ve been dealing with huge log files lately, several GBs in size, and got frustrated with common tools such as Notepad++ freezing and crashing a server with limited RAM. I wanted something fast, relatively lightweight, and install-free for Windows. I have been searched around the web for a while but couldn't find an equivalent, so I built Giant Log Viewer.<p>Features:<p>- It is an equivalent of the Linux' `less` pager tool for Windows, but works on Linux and macOS too<p>- Opens multi-GB UTF-8 unstructured log/text files instantly (without loading everything into memory)<p>- Navigates through the file using keyboard shortcuts -- jump to the end of the file, go to previous line, etc.<p>- Limited text/regular expression searching<p>- Portable -- just unzip and run<p>- Open-source -- welcome to contribute or fork<p>Would love your feedback, and if it’s useful, feel free to star it, suggest improvements or even contribute! Depends on the needs of myself and other users, more features might be added.
Show HN: Torque – A lightweight meta-assembler for any processor
Hello everyone, I've been working on this project for the past few months.<p>Torque is a meta-assembler: instead of having an instruction set built into the assembler, you use macros to build up a small language that decribes an instruction set and then you use that to write your program. It's designed to work for any microcontroller/processor architecture, you build from the bit level upwards so there aren't any assumptions around word widths, instruction formats, or endianness.<p>I created Torque initially to write programs for a PIC microcontroller, after running into difficulties with the official assembler. I've also used it to write programs for the Z80 processor inside an old TRS-80 computer.<p>Let me know if you try it out or have any questions!
Show HN: Torque – A lightweight meta-assembler for any processor
Hello everyone, I've been working on this project for the past few months.<p>Torque is a meta-assembler: instead of having an instruction set built into the assembler, you use macros to build up a small language that decribes an instruction set and then you use that to write your program. It's designed to work for any microcontroller/processor architecture, you build from the bit level upwards so there aren't any assumptions around word widths, instruction formats, or endianness.<p>I created Torque initially to write programs for a PIC microcontroller, after running into difficulties with the official assembler. I've also used it to write programs for the Z80 processor inside an old TRS-80 computer.<p>Let me know if you try it out or have any questions!
Show HN: Torque – A lightweight meta-assembler for any processor
Hello everyone, I've been working on this project for the past few months.<p>Torque is a meta-assembler: instead of having an instruction set built into the assembler, you use macros to build up a small language that decribes an instruction set and then you use that to write your program. It's designed to work for any microcontroller/processor architecture, you build from the bit level upwards so there aren't any assumptions around word widths, instruction formats, or endianness.<p>I created Torque initially to write programs for a PIC microcontroller, after running into difficulties with the official assembler. I've also used it to write programs for the Z80 processor inside an old TRS-80 computer.<p>Let me know if you try it out or have any questions!
Show HN: Resonate – real-time high temporal resolution spectral analysis
Show HN: Resonate – real-time high temporal resolution spectral analysis
Show HN: Resonate – real-time high temporal resolution spectral analysis
Show HN: MCP-Shield – Detect security issues in MCP servers
I noticed the growing security concerns around MCP (<a href="https://news.ycombinator.com/item?id=43600192">https://news.ycombinator.com/item?id=43600192</a>) and built an open source tool that can detect several patterns of tool poisoning attacks, exfiltration channels and cross-origin manipulations.<p>MCP-Shield scans your installed servers (Cursor, Claude Desktop, etc.) and shows what each tool is trying to do at the instruction level, beyond just the API surface. It catches hidden instructions that try to read sensitive files, shadow other tools' behavior, or exfiltrate data.<p>Example of what it detects:<p>- Hidden instructions attempting to access ~/.ssh/id_rsa<p>- Cross-origin manipulations between server that can redirect WhatsApp messages<p>- Tool shadowing that overrides behavior of other MCP tools<p>- Potential exfiltration channels through optional parameters<p>I've included clear examples of detection outputs in the README and multiple example vulnerabilities in the repo so you can see the kinds of things it catches.<p>This is an early version, but I'd appreciate feedback from the community, especially around detection patterns and false positives.
Show HN: MCP-Shield – Detect security issues in MCP servers
I noticed the growing security concerns around MCP (<a href="https://news.ycombinator.com/item?id=43600192">https://news.ycombinator.com/item?id=43600192</a>) and built an open source tool that can detect several patterns of tool poisoning attacks, exfiltration channels and cross-origin manipulations.<p>MCP-Shield scans your installed servers (Cursor, Claude Desktop, etc.) and shows what each tool is trying to do at the instruction level, beyond just the API surface. It catches hidden instructions that try to read sensitive files, shadow other tools' behavior, or exfiltrate data.<p>Example of what it detects:<p>- Hidden instructions attempting to access ~/.ssh/id_rsa<p>- Cross-origin manipulations between server that can redirect WhatsApp messages<p>- Tool shadowing that overrides behavior of other MCP tools<p>- Potential exfiltration channels through optional parameters<p>I've included clear examples of detection outputs in the README and multiple example vulnerabilities in the repo so you can see the kinds of things it catches.<p>This is an early version, but I'd appreciate feedback from the community, especially around detection patterns and false positives.
Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator
Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator
Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator
Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator
Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator