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Show HN: LoopMix128 – Fast C PRNG (.46ns), 2^128 Period, BigCrush/PractRand Pass

LoopMix128 is a fast C PRNG I wrote for non-cryptographic tasks.<p>GitHub (MIT): <a href="https://github.com/danielcota/LoopMix128">https://github.com/danielcota/LoopMix128</a><p>Highlights:<p>* ~0.37 ns/value (GCC 11.4, -O3 -march=native), 98% faster than xoroshiro128++ and PCG64.<p>* Passes TestU01 BigCrush & PractRand (32TB).<p>* Guaranteed 2^128 period.<p>* Proven injective (192-bit state) via Z3 SMT solver; allows parallel streams.<p>* Core requires only stdint.h.<p>Seeking feedback on design, use cases, or further testing.

Show HN: Code Claude Code

In the nature of Open Source, I am releasing something I'm actively working on but is insanely simple and will likely be made anyways.<p>It is an SDK for scripting Claude Code.<p>It's a lightweight (155 lines) and free wrapper around claude code<p>This is a big deal because it seems that using claude code and cursor has become largly repitive. My workflow typically goes like this:<p>Plan out my task into a file, then have claude code implement the plan into my code.<p>I'm actively building a product with this, but still wanted to make it OSS!<p>Use it now with `pip install codesys`

Show HN: Code Claude Code

In the nature of Open Source, I am releasing something I'm actively working on but is insanely simple and will likely be made anyways.<p>It is an SDK for scripting Claude Code.<p>It's a lightweight (155 lines) and free wrapper around claude code<p>This is a big deal because it seems that using claude code and cursor has become largly repitive. My workflow typically goes like this:<p>Plan out my task into a file, then have claude code implement the plan into my code.<p>I'm actively building a product with this, but still wanted to make it OSS!<p>Use it now with `pip install codesys`

Show HN: Code Claude Code

In the nature of Open Source, I am releasing something I'm actively working on but is insanely simple and will likely be made anyways.<p>It is an SDK for scripting Claude Code.<p>It's a lightweight (155 lines) and free wrapper around claude code<p>This is a big deal because it seems that using claude code and cursor has become largly repitive. My workflow typically goes like this:<p>Plan out my task into a file, then have claude code implement the plan into my code.<p>I'm actively building a product with this, but still wanted to make it OSS!<p>Use it now with `pip install codesys`

Show HN: Xenolab – Rasp Pi monitor for my pet carnivourus plants

Show HN: Xenolab – Rasp Pi monitor for my pet carnivourus plants

Show HN: Xenolab – Rasp Pi monitor for my pet carnivourus plants

Show HN: BlenderQ – A TUI for managing multiple Blender renders

Hi HN,<p>I’m a solo content-creator/Blender user and developed this tool as an easy way to manage and render multiple Blender renders locally.<p>The TUI portion is written in TypeScript because it gave me a good way to build the front end that allowed for some complex components in a language that I was intimately familiar with, and the portions that interact with Blender are actually Python scripts.

Show HN: BlenderQ – A TUI for managing multiple Blender renders

Hi HN,<p>I’m a solo content-creator/Blender user and developed this tool as an easy way to manage and render multiple Blender renders locally.<p>The TUI portion is written in TypeScript because it gave me a good way to build the front end that allowed for some complex components in a language that I was intimately familiar with, and the portions that interact with Blender are actually Python scripts.

Show HN: Hydra (YC W22) – Serverless Analytics on Postgres

Hi HN, Hydra cofounders (Joe and JD) here (<a href="https://www.hydra.so/">https://www.hydra.so/</a>)! We enable realtime analytics on Postgres without requiring an external analytics database.<p>Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database.<p>(A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.)<p>For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: (<a href="https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-features.html" rel="nofollow">https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported...</a>).<p>We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables.<p>Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database.<p>The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. The database automatically provisions and isolates dedicated compute resources for each query process. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time.<p>How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process.<p>why is hydra so fast at analytics? (<a href="https://tinyurl.com/hydraDBMS" rel="nofollow">https://tinyurl.com/hydraDBMS</a>) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching<p>what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql.<p>example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable.<p>Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project.

Show HN: Hydra (YC W22) – Serverless Analytics on Postgres

Hi HN, Hydra cofounders (Joe and JD) here (<a href="https://www.hydra.so/">https://www.hydra.so/</a>)! We enable realtime analytics on Postgres without requiring an external analytics database.<p>Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database.<p>(A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.)<p>For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: (<a href="https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-features.html" rel="nofollow">https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported...</a>).<p>We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables.<p>Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database.<p>The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. The database automatically provisions and isolates dedicated compute resources for each query process. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time.<p>How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process.<p>why is hydra so fast at analytics? (<a href="https://tinyurl.com/hydraDBMS" rel="nofollow">https://tinyurl.com/hydraDBMS</a>) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching<p>what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql.<p>example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable.<p>Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project.

Show HN: Hydra (YC W22) – Serverless Analytics on Postgres

Hi HN, Hydra cofounders (Joe and JD) here (<a href="https://www.hydra.so/">https://www.hydra.so/</a>)! We enable realtime analytics on Postgres without requiring an external analytics database.<p>Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database.<p>(A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.)<p>For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: (<a href="https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-features.html" rel="nofollow">https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported...</a>).<p>We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables.<p>Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database.<p>The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. The database automatically provisions and isolates dedicated compute resources for each query process. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time.<p>How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process.<p>why is hydra so fast at analytics? (<a href="https://tinyurl.com/hydraDBMS" rel="nofollow">https://tinyurl.com/hydraDBMS</a>) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching<p>what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql.<p>example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable.<p>Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project.

Show HN: A backend agnostic Ruby framework for building reactive desktop apps

For a year or two I've been building a UI library with the aim of making desktop applications fun and easy to write.<p>It is currently written in C/Ruby and uses a custom tree sitter grammar to describe templates.<p>Features include: composable UI components, template directives, event and prop handling, slots, styles and automation capabilities baked in.<p>One of the goals of the project is privileging easy to write custom components via a drawing API over providing a fixed set of components.<p>At the time of this writing it should install on Windows, Mac, and Linux, but sometimes it is hard to test the install on all platforms.<p>I'd love to hear any feedback or suggestions on the project. It is still in an early stage, but it would be great to hear thoughts.<p>Docs are here: <a href="https://hokusai.skinnyjames.net/" rel="nofollow">https://hokusai.skinnyjames.net/</a> Repo is here: <a href="https://codeberg.org/skinnyjames/hokusai" rel="nofollow">https://codeberg.org/skinnyjames/hokusai</a><p>Licensed under the PPL

Show HN: A backend agnostic Ruby framework for building reactive desktop apps

For a year or two I've been building a UI library with the aim of making desktop applications fun and easy to write.<p>It is currently written in C/Ruby and uses a custom tree sitter grammar to describe templates.<p>Features include: composable UI components, template directives, event and prop handling, slots, styles and automation capabilities baked in.<p>One of the goals of the project is privileging easy to write custom components via a drawing API over providing a fixed set of components.<p>At the time of this writing it should install on Windows, Mac, and Linux, but sometimes it is hard to test the install on all platforms.<p>I'd love to hear any feedback or suggestions on the project. It is still in an early stage, but it would be great to hear thoughts.<p>Docs are here: <a href="https://hokusai.skinnyjames.net/" rel="nofollow">https://hokusai.skinnyjames.net/</a> Repo is here: <a href="https://codeberg.org/skinnyjames/hokusai" rel="nofollow">https://codeberg.org/skinnyjames/hokusai</a><p>Licensed under the PPL

Show HN: A backend agnostic Ruby framework for building reactive desktop apps

For a year or two I've been building a UI library with the aim of making desktop applications fun and easy to write.<p>It is currently written in C/Ruby and uses a custom tree sitter grammar to describe templates.<p>Features include: composable UI components, template directives, event and prop handling, slots, styles and automation capabilities baked in.<p>One of the goals of the project is privileging easy to write custom components via a drawing API over providing a fixed set of components.<p>At the time of this writing it should install on Windows, Mac, and Linux, but sometimes it is hard to test the install on all platforms.<p>I'd love to hear any feedback or suggestions on the project. It is still in an early stage, but it would be great to hear thoughts.<p>Docs are here: <a href="https://hokusai.skinnyjames.net/" rel="nofollow">https://hokusai.skinnyjames.net/</a> Repo is here: <a href="https://codeberg.org/skinnyjames/hokusai" rel="nofollow">https://codeberg.org/skinnyjames/hokusai</a><p>Licensed under the PPL

Show HN: Oliphaunt – A native Mastodon client for macOS

I’ve been building Oliphaunt, a native Mastodon client for macOS, as a solo project — designed to be fast, lightweight and feel right at home on the Mac. It’s not built with Catalyst or Electron framework.<p>Key features:<p>• Native macOS UI using AppKit with some SwiftUI integration (not a web wrapper)<p>• Core Data for local caching<p>• Responsive, keyboard-friendly interface<p>• UX tailored for desktop-class Mac computers<p>• Supports multiple accounts, cross-instance timelines and search<p>You can try it via TestFlight (macOS 14+ Sonoma): <a href="https://testflight.apple.com/join/Epq1P3Cw" rel="nofollow">https://testflight.apple.com/join/Epq1P3Cw</a><p>Feedback is welcome here, on GitHub, or via TestFlight: <a href="https://github.com/anosidium/Oliphaunt-Feedback-And-Support">https://github.com/anosidium/Oliphaunt-Feedback-And-Support</a>

Show HN: Oliphaunt – A native Mastodon client for macOS

I’ve been building Oliphaunt, a native Mastodon client for macOS, as a solo project — designed to be fast, lightweight and feel right at home on the Mac. It’s not built with Catalyst or Electron framework.<p>Key features:<p>• Native macOS UI using AppKit with some SwiftUI integration (not a web wrapper)<p>• Core Data for local caching<p>• Responsive, keyboard-friendly interface<p>• UX tailored for desktop-class Mac computers<p>• Supports multiple accounts, cross-instance timelines and search<p>You can try it via TestFlight (macOS 14+ Sonoma): <a href="https://testflight.apple.com/join/Epq1P3Cw" rel="nofollow">https://testflight.apple.com/join/Epq1P3Cw</a><p>Feedback is welcome here, on GitHub, or via TestFlight: <a href="https://github.com/anosidium/Oliphaunt-Feedback-And-Support">https://github.com/anosidium/Oliphaunt-Feedback-And-Support</a>

Show HN: Oliphaunt – A native Mastodon client for macOS

I’ve been building Oliphaunt, a native Mastodon client for macOS, as a solo project — designed to be fast, lightweight and feel right at home on the Mac. It’s not built with Catalyst or Electron framework.<p>Key features:<p>• Native macOS UI using AppKit with some SwiftUI integration (not a web wrapper)<p>• Core Data for local caching<p>• Responsive, keyboard-friendly interface<p>• UX tailored for desktop-class Mac computers<p>• Supports multiple accounts, cross-instance timelines and search<p>You can try it via TestFlight (macOS 14+ Sonoma): <a href="https://testflight.apple.com/join/Epq1P3Cw" rel="nofollow">https://testflight.apple.com/join/Epq1P3Cw</a><p>Feedback is welcome here, on GitHub, or via TestFlight: <a href="https://github.com/anosidium/Oliphaunt-Feedback-And-Support">https://github.com/anosidium/Oliphaunt-Feedback-And-Support</a>

Show HN: Oliphaunt – A native Mastodon client for macOS

I’ve been building Oliphaunt, a native Mastodon client for macOS, as a solo project — designed to be fast, lightweight and feel right at home on the Mac. It’s not built with Catalyst or Electron framework.<p>Key features:<p>• Native macOS UI using AppKit with some SwiftUI integration (not a web wrapper)<p>• Core Data for local caching<p>• Responsive, keyboard-friendly interface<p>• UX tailored for desktop-class Mac computers<p>• Supports multiple accounts, cross-instance timelines and search<p>You can try it via TestFlight (macOS 14+ Sonoma): <a href="https://testflight.apple.com/join/Epq1P3Cw" rel="nofollow">https://testflight.apple.com/join/Epq1P3Cw</a><p>Feedback is welcome here, on GitHub, or via TestFlight: <a href="https://github.com/anosidium/Oliphaunt-Feedback-And-Support">https://github.com/anosidium/Oliphaunt-Feedback-And-Support</a>

Show HN: Hyper – Standards-first React alternative

Author here. This is an attempt to simplify frontend development:<p>1. Standards first: User interfaces should be assembled with HTML, styled with CSS, and enhanced with JavaScript.<p>2. Less abstractions: UI composition should be easy and require as few idioms and abstractions as possible, both on client and server.<p>3. Design Systems: Design should be a separate subsystem, easily accessible for developers who care about and understand design.<p>4. Scalability: Complex UIs should retain simplicity as the application grows.<p>Eager to hear your thoughts!