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Show HN: Lander, a lunar lander style web game
I’ve been working on this game for the past few weeks. It’s written in plain JavaScript, mostly with canvas, with no dependencies.<p>The code is here: <a href="https://github.com/ehmorris/lunar-lander">https://github.com/ehmorris/lunar-lander</a>
Show HN: Lander, a lunar lander style web game
I’ve been working on this game for the past few weeks. It’s written in plain JavaScript, mostly with canvas, with no dependencies.<p>The code is here: <a href="https://github.com/ehmorris/lunar-lander">https://github.com/ehmorris/lunar-lander</a>
Show HN: Lander, a lunar lander style web game
I’ve been working on this game for the past few weeks. It’s written in plain JavaScript, mostly with canvas, with no dependencies.<p>The code is here: <a href="https://github.com/ehmorris/lunar-lander">https://github.com/ehmorris/lunar-lander</a>
Show HN: Lander, a lunar lander style web game
I’ve been working on this game for the past few weeks. It’s written in plain JavaScript, mostly with canvas, with no dependencies.<p>The code is here: <a href="https://github.com/ehmorris/lunar-lander">https://github.com/ehmorris/lunar-lander</a>
Show HN: Hire from Communities (Fromcommunity.com)
Get your best resources from well-known communities within the industry, such as forums, online groups, or active community channels, where they have built a reputation for their skills and contributions.<p>We are happy to get feedback and join forces :)
Show HN: A Unix timestamp converter that includes the micro and nanoseconds
I find myself having to convert a lot of unix timestamps to a human readable format, but most of the online calculators I found don't include the micro or nanoseconds in the human readable output. It's a small detail, but I find myself having to manually space out the timestamp frequently. I'm learning frontend development, so I made this converter to hopefully make it easier to convert timestamps. Welcome to any feedback :)
Show HN: Watch ChatGPT debate itself on a given topic
Show HN: Watch ChatGPT debate itself on a given topic
Show HN: Watch ChatGPT debate itself on a given topic
Show HN: Procal: A simple Qt-based programming calculator
Show HN: Procal: A simple Qt-based programming calculator
Show HN: Procal: A simple Qt-based programming calculator
Show HN: Practice Job Interview with ChatGPT API
Hi HN!<p>ChatGPT API was just released and I'm eager to hack something with it.<p>So I created PracticeInterview.co to help people practice for their next job interview.<p>ChatGPT acts as the interviewer and you can choose to interview for various roles such as Finance Manager, Social Media Manager, and Software Engineer.<p>I think this is a cool implementation of ChatGPT and the interview sessions so far has been pretty coherent.<p>Please give it a try! It's free, no logins required, and your interview session is saved so you can comeback to it later.<p>Since it's an MVP, I would welcome all feedback. Thanks HN!
Show HN: Zipslicer, a library for loading LLM checkpoints on consumer hardware
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is.<p>It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one tensor at a time.<p>I describe the rationale and technical details of the library's design in the blogpost: <a href="https://kir-gadjello.github.io/posts/zipslicer/" rel="nofollow">https://kir-gadjello.github.io/posts/zipslicer/</a>
Show HN: Zipslicer, a library for loading LLM checkpoints on consumer hardware
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is.<p>It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one tensor at a time.<p>I describe the rationale and technical details of the library's design in the blogpost: <a href="https://kir-gadjello.github.io/posts/zipslicer/" rel="nofollow">https://kir-gadjello.github.io/posts/zipslicer/</a>
Show HN: Nosey Parker, a fast and low-noise secrets detector for textual data
Nosey Parker is an Apache-licensed command-line tool that finds secrets and sensitive information in textual data. It's useful both for offensive and defensive security testing.<p>The big idea: textual content in, hardcoded secrets out. These include things like API keys and passwords.<p>It should do a reasonable job on any textual input. It will recursively scan any directories it is pointed at. It also has special support for scanning the complete contents of Git repositories.<p>The default rules in Nosey Parker have been carefully chosen to minimize false positives. Many API tokens these days have well-specified formats that are amenable to precise matching with regular expressions, and these are the kind of things that the default rules detect. Additionally, its findings are deduplicated. Together, these give much higher signal-to-noise compared to similar tools.<p>Nosey Parker is fast: it can scan 100GB of Linux kernel commit history in just over a minute on my laptop. This speed comes from several factors, but most significantly from using the amazing Hyperscan library for simultaneous matching of all regex rules in a single pass. In comparison with similar tools on large inputs, Nosey Parker is usually 1-3 orders of magnitude faster.<p>Nosey Parker was originally created to help construct a labeled dataset of secrets for machine learning purposes. But it proved surprisingly useful on its own. In the past year, an internal, proprietary version (with added machine learning capabilities) has been regularly used in security engagements at Praetorian.<p>In late 2022, Nosey Parker was reimplemented in Rust, released as open-source, and presented at Black Hat Arsenal.<p>It now supports enumeration and scanning of GitHub repositories by providing just usernames or organization names. It also recently got support for SARIF output, which several other tools understand.
Show HN: Nosey Parker, a fast and low-noise secrets detector for textual data
Nosey Parker is an Apache-licensed command-line tool that finds secrets and sensitive information in textual data. It's useful both for offensive and defensive security testing.<p>The big idea: textual content in, hardcoded secrets out. These include things like API keys and passwords.<p>It should do a reasonable job on any textual input. It will recursively scan any directories it is pointed at. It also has special support for scanning the complete contents of Git repositories.<p>The default rules in Nosey Parker have been carefully chosen to minimize false positives. Many API tokens these days have well-specified formats that are amenable to precise matching with regular expressions, and these are the kind of things that the default rules detect. Additionally, its findings are deduplicated. Together, these give much higher signal-to-noise compared to similar tools.<p>Nosey Parker is fast: it can scan 100GB of Linux kernel commit history in just over a minute on my laptop. This speed comes from several factors, but most significantly from using the amazing Hyperscan library for simultaneous matching of all regex rules in a single pass. In comparison with similar tools on large inputs, Nosey Parker is usually 1-3 orders of magnitude faster.<p>Nosey Parker was originally created to help construct a labeled dataset of secrets for machine learning purposes. But it proved surprisingly useful on its own. In the past year, an internal, proprietary version (with added machine learning capabilities) has been regularly used in security engagements at Praetorian.<p>In late 2022, Nosey Parker was reimplemented in Rust, released as open-source, and presented at Black Hat Arsenal.<p>It now supports enumeration and scanning of GitHub repositories by providing just usernames or organization names. It also recently got support for SARIF output, which several other tools understand.
Show HN: Classic FPS Wolfenstein 3D brought in the browser via Emscripten
Show HN: Classic FPS Wolfenstein 3D brought in the browser via Emscripten
Show HN: Classic FPS Wolfenstein 3D brought in the browser via Emscripten