Automatically Packaging a Haskell Library as a Swift Binary XCFramework

Show HN: Notepad.exe – macOS editor for Swift and Python (now Linux runtime)

Show HN: Notepad.exe – macOS editor for Swift and Python (now Linux runtime) I recently released version 1.4 of Notepad.exe, my editor built for macOS. The goal of the app is to let you prototype ideas in Swift or Python with minimal setup - write code, hit Run, skip project scaffolding.This release adds support for a Linux runtime/subsystem, so you can write on macOS and execute snippets in a Linux environment.I’d love to hear any feedback or answer any questions: would a tool like this fit your workflow? What friction remains?

Better Curl Saul: a lightweight API testing CLI focused on UX and simplicity

For Good First Issue – A repository of social impact and open source projects

Show HN: Bottlefire – Build single-executable microVMs from Docker images

Bypass PostgreSQL catalog overhead with direct partition hash calculations

Show HN: Improving RAG with chess Elo scores

Show HN: Improving RAG with chess Elo scores Hello HN,I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents.We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0.We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps: * Collect soft preferences between pairs of documents using an ensemble of LLMs. * Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into absolute per-document scores. * Normalize relevance scores across queries using a bias correction step, modeled using cross-query comparisons and solved with MLE.You can try the models either through our API (<a href="https://docs.zeroentropy.dev/models">https://docs.zeroentropy.dev/models</a>), or via HuggingFace (<a href="https://huggingface.co/zeroentropy/zerank-1-small" rel="nofollow">https://huggingface.co/zeroentropy/zerank-1-small</a>).We would love this community's feedback on the models, and the training approach. A full technical report is also going to be released soon.Thank you!

Show HN: Unlearning Comparator, a visual tool to compare machine unlearning

Show HN: Unlearning Comparator, a visual tool to compare machine unlearning I built Unlearning Comparator, a visual analytics toolkit to help researchers and developers compare how different machine unlearning methods work. It provides a unified workflow to test for accuracy, efficiency, and privacy. You can check out the live demo linked in the post, and the source code is on GitHub: <a href="https://github.com/gnueaj/Machine-Unlearning-Comparator">https://github.com/gnueaj/Machine-Unlearning-Comparator</a> Our accompanying paper is currently under review at IEEE TVCG. Happy to answer any questions and would love to hear your feedback!