The best Hacker News stories from Show from the past day
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Show HN: Hacker News LCD Badge
Show HN: Hacker News LCD Badge
Show HN: Terminal Based Wikipedia
Show HN: Terminal Based Wikipedia
Show HN: Terminal Based Wikipedia
Show HN: Browse and Generate AI Memes for Free
Show HN: Browse and Generate AI Memes for Free
Show HN: Browse and Generate AI Memes for Free
Show HN: Find the most climate friendly meeting location
Just enter the locations people will be traveling from. MLC then calculates the location, where the combined aircraft emissions are minimised. Based on data from the European Emissions Agency.
Show HN: Find the most climate friendly meeting location
Just enter the locations people will be traveling from. MLC then calculates the location, where the combined aircraft emissions are minimised. Based on data from the European Emissions Agency.
Show HN: JazzKeys.fyi – Tutorials for jazz and funk keyboard players
Web developer and jazz piano player here. I’ve just launched JazzKeys.fyi (<a href="https://JazzKeys.fyi" rel="nofollow">https://JazzKeys.fyi</a>), a website of tutorials covering bebop, modern jazz, blues and funk. It’s more or less a brain dump of some of what I’ve learned over the course of playing and studying jazz for ~25 years. I would likely have found it useful back when I started, and I created it in the hope that it might be helpful to others.<p>The main audience for the site is people who already have some facility in playing a keyboard instrument (in any genre), and who want to learn jazz or improve their jazz playing. It assumes a basic understanding of how scales and chords work.<p>Building the animated keyboard that accompanies each musical example was quite a rewarding technical challenge. For anyone interested in the details, there is a blog post about it at <a href="https://www.jamieonkeys.dev/posts/piano-keyboard-javascript/" rel="nofollow">https://www.jamieonkeys.dev/posts/piano-keyboard-javascript/</a>.<p>I hope folk find the site to be a helpful resource. Feedback and bug reports welcome!
Show HN: SearQ - A REST API that allows users to search from RSS feeds
Show HN: Safe Data Changes in PostgreSQL
Hi HN, we're excited to share our open source tool with the community! We previously posted here with the tagline “real-time events for Postgres” [0]. But after feedback from early users and the community, we’ve shifted our focus to working on tooling for manual database changes.<p>We've consistently heard teams describe challenges with the way manual data updates are handled. Seemingly every engineer we spoke with had examples of errant queries that ended up causing significant harm in production environments (data loss/service interruptions).<p>We’ve seen a few different approaches to how changes to production databases occur today:<p>Option 1: all engineers have production write access (highest speed, highest risk)<p>Option 2: one or a few engineers have write access (medium speed, high risk)<p>Option 3: engineers request temporary access to make changes (low speed, medium risk)<p>Option 4: all updates are checked into version control and run manually or through CI/CD (low speed, low risk)<p>Option 5: no manual updates are made - all changes must go through an internal endpoint (lowest speed, lowest risk)<p>Our goal is to enable high speed changes with the lowest risk possible. We’re planning to do this by providing an open-source toolkit for safeguarding databases, including the following features:<p>- Alerts (available now): Receive notifications any time a manual change occurs<p>- Audit History (beta): View all historical manual changes with context<p>- Query Preview (coming soon): Preview affected rows and query plan prior to running changes<p>- Approval Flow (coming soon): Require query review before a change can be run<p>We’re starting with alerts. Teams can receive Slack notifications anytime an INSERT, UPDATE, or DELETE is executed from a non-application database user. While this doesn’t prevent issues from occurring, it does enable an initial level of traceability and understanding who made an update, what data was changed, and when it occurred.<p>We’d love to hear feedback from the HN community on how you’ve seen database changes handled, pain points you’ve experienced with data change processes, or generally any feedback on our thinking and approach.<p>[0] <a href="https://news.ycombinator.com/item?id=34828169" rel="nofollow">https://news.ycombinator.com/item?id=34828169</a>
Show HN: Safe Data Changes in PostgreSQL
Hi HN, we're excited to share our open source tool with the community! We previously posted here with the tagline “real-time events for Postgres” [0]. But after feedback from early users and the community, we’ve shifted our focus to working on tooling for manual database changes.<p>We've consistently heard teams describe challenges with the way manual data updates are handled. Seemingly every engineer we spoke with had examples of errant queries that ended up causing significant harm in production environments (data loss/service interruptions).<p>We’ve seen a few different approaches to how changes to production databases occur today:<p>Option 1: all engineers have production write access (highest speed, highest risk)<p>Option 2: one or a few engineers have write access (medium speed, high risk)<p>Option 3: engineers request temporary access to make changes (low speed, medium risk)<p>Option 4: all updates are checked into version control and run manually or through CI/CD (low speed, low risk)<p>Option 5: no manual updates are made - all changes must go through an internal endpoint (lowest speed, lowest risk)<p>Our goal is to enable high speed changes with the lowest risk possible. We’re planning to do this by providing an open-source toolkit for safeguarding databases, including the following features:<p>- Alerts (available now): Receive notifications any time a manual change occurs<p>- Audit History (beta): View all historical manual changes with context<p>- Query Preview (coming soon): Preview affected rows and query plan prior to running changes<p>- Approval Flow (coming soon): Require query review before a change can be run<p>We’re starting with alerts. Teams can receive Slack notifications anytime an INSERT, UPDATE, or DELETE is executed from a non-application database user. While this doesn’t prevent issues from occurring, it does enable an initial level of traceability and understanding who made an update, what data was changed, and when it occurred.<p>We’d love to hear feedback from the HN community on how you’ve seen database changes handled, pain points you’ve experienced with data change processes, or generally any feedback on our thinking and approach.<p>[0] <a href="https://news.ycombinator.com/item?id=34828169" rel="nofollow">https://news.ycombinator.com/item?id=34828169</a>
Show HN: I built an autopilot for the lunar lander game
I got pretty good at (and very addicted to) the lunar lander game from a few days ago...<p>so I decided to make an autopilot for the lander based on what I felt like was the best strategy! Now I can have perfect landings every time without lifting a finger :D<p>Writing the autopilot code was a lot more fun than I expected! It felt a bit like programming a robot.<p>Source code: <a href="https://github.com/szhu/lunar-lander-autopilot">https://github.com/szhu/lunar-lander-autopilot</a><p>Original lander HN post: <a href="https://news.ycombinator.com/item?id=35032506" rel="nofollow">https://news.ycombinator.com/item?id=35032506</a>
Show HN: I built an autopilot for the lunar lander game
I got pretty good at (and very addicted to) the lunar lander game from a few days ago...<p>so I decided to make an autopilot for the lander based on what I felt like was the best strategy! Now I can have perfect landings every time without lifting a finger :D<p>Writing the autopilot code was a lot more fun than I expected! It felt a bit like programming a robot.<p>Source code: <a href="https://github.com/szhu/lunar-lander-autopilot">https://github.com/szhu/lunar-lander-autopilot</a><p>Original lander HN post: <a href="https://news.ycombinator.com/item?id=35032506" rel="nofollow">https://news.ycombinator.com/item?id=35032506</a>
Show HN: I built an autopilot for the lunar lander game
I got pretty good at (and very addicted to) the lunar lander game from a few days ago...<p>so I decided to make an autopilot for the lander based on what I felt like was the best strategy! Now I can have perfect landings every time without lifting a finger :D<p>Writing the autopilot code was a lot more fun than I expected! It felt a bit like programming a robot.<p>Source code: <a href="https://github.com/szhu/lunar-lander-autopilot">https://github.com/szhu/lunar-lander-autopilot</a><p>Original lander HN post: <a href="https://news.ycombinator.com/item?id=35032506" rel="nofollow">https://news.ycombinator.com/item?id=35032506</a>
Show HN: PyBroker – Algotrading in Python with Machine Learning
Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance.<p>Some of the key features of PyBroker include:<p>- A super-fast backtesting engine built using NumPy and accelerated with Numba.<p>- The ability to create and execute trading rules and models across multiple instruments with ease.<p>- Access to historical data from Alpaca and Yahoo Finance.<p>- The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading. The basic concept behind Walkforward Analysis is that it splits your historical data into multiple time windows and then "walks forward" in time in the same way that the strategy would be executed and retrained on new data in the real world. Walkforward Analysis also helps overcome the problem of data mining and overfitting by testing your strategy on out-of-sample data.<p>- More reliable trading metrics that use randomized bootstrapping to provide more accurate results. PyBroker calculates metrics such as Sharpe, Profit Factor, and max drawdown using bootstrapping, which randomly samples your strategy's returns to simulate thousands of alternate scenarios that could have happened. This allows you to test for statistical significance and have more confidence in the effectiveness of your strategy.<p>- Support for strategies that use ranking and flexible position sizing.<p>- Caching of downloaded data, indicators, and models to speed up your development process.<p>- Parallelized computations that enable faster performance.<p>- Additionally, I have written tutorials on the framework and some general algorithmic trading concepts that can be found on <a href="https://www.pybroker.com" rel="nofollow">https://www.pybroker.com</a>. All of the code is available on Github using the link above.<p>Thanks for reading!
Show HN: PyBroker – Algotrading in Python with Machine Learning
Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance.<p>Some of the key features of PyBroker include:<p>- A super-fast backtesting engine built using NumPy and accelerated with Numba.<p>- The ability to create and execute trading rules and models across multiple instruments with ease.<p>- Access to historical data from Alpaca and Yahoo Finance.<p>- The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading. The basic concept behind Walkforward Analysis is that it splits your historical data into multiple time windows and then "walks forward" in time in the same way that the strategy would be executed and retrained on new data in the real world. Walkforward Analysis also helps overcome the problem of data mining and overfitting by testing your strategy on out-of-sample data.<p>- More reliable trading metrics that use randomized bootstrapping to provide more accurate results. PyBroker calculates metrics such as Sharpe, Profit Factor, and max drawdown using bootstrapping, which randomly samples your strategy's returns to simulate thousands of alternate scenarios that could have happened. This allows you to test for statistical significance and have more confidence in the effectiveness of your strategy.<p>- Support for strategies that use ranking and flexible position sizing.<p>- Caching of downloaded data, indicators, and models to speed up your development process.<p>- Parallelized computations that enable faster performance.<p>- Additionally, I have written tutorials on the framework and some general algorithmic trading concepts that can be found on <a href="https://www.pybroker.com" rel="nofollow">https://www.pybroker.com</a>. All of the code is available on Github using the link above.<p>Thanks for reading!
Show HN: PyBroker – Algotrading in Python with Machine Learning
Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance.<p>Some of the key features of PyBroker include:<p>- A super-fast backtesting engine built using NumPy and accelerated with Numba.<p>- The ability to create and execute trading rules and models across multiple instruments with ease.<p>- Access to historical data from Alpaca and Yahoo Finance.<p>- The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading. The basic concept behind Walkforward Analysis is that it splits your historical data into multiple time windows and then "walks forward" in time in the same way that the strategy would be executed and retrained on new data in the real world. Walkforward Analysis also helps overcome the problem of data mining and overfitting by testing your strategy on out-of-sample data.<p>- More reliable trading metrics that use randomized bootstrapping to provide more accurate results. PyBroker calculates metrics such as Sharpe, Profit Factor, and max drawdown using bootstrapping, which randomly samples your strategy's returns to simulate thousands of alternate scenarios that could have happened. This allows you to test for statistical significance and have more confidence in the effectiveness of your strategy.<p>- Support for strategies that use ranking and flexible position sizing.<p>- Caching of downloaded data, indicators, and models to speed up your development process.<p>- Parallelized computations that enable faster performance.<p>- Additionally, I have written tutorials on the framework and some general algorithmic trading concepts that can be found on <a href="https://www.pybroker.com" rel="nofollow">https://www.pybroker.com</a>. All of the code is available on Github using the link above.<p>Thanks for reading!