Show HN: Frcntl – community for fractional developers and designers https://ift.tt/K0MdavQ

Show HN: Frcntl – community for fractional developers and designers Hey HN! In 2017, I launched Moonlight to help engineers find part-time, remote jobs. Combining part-time with remote work was a big leap then, so we quickly narrowed our focus to just remote work. Lately, I've seen many talented friends and colleagues moving into part-time fractional roles. They're carving out more personal time while maintaining the same income. Personally, fractional work has empowered me to pursue my entrepreneurial goals without worrying about runway or funding. It's no surprise that hiring managers are gravitating towards this, especially as companies are emphasizing efficiency instead of all-out growth. From our experience with Moonlight, we found that most engineering managers prefer hiring for ongoing, direct work relationships rather than transient, fixed-scope projects. These enduring relationships proved to be mutually beneficial, providing stability for companies and workers in a working style legible to both. Today I'm launching FRCTNL - a community of fractional developers, designers, and marketers. Distinct from traditional marketplaces focused on top-down projects, FRCTNL focuses on often-overlooked bottoms-up, relationship-centric "staff augmentation" roles. Hiring managers seek quality candidates referred by their existing team, and direct relationships with talent — this is the niche FRCTNL aims to serve. FRCTNL operates on a referral model, where members help swap and share opportunities with each other. The underlying hypothesis is that there is latent demand for fractional talent, and that connecting with existing fractional workers is the best way to find those open roles. I'm convinced that our fractional worker network will streamline hiring for companies and further mainstream part-time roles. Take a look, and let me know if you have any questions or feedback! https://www.frctnl.xyz/ September 7, 2023 at 08:26PM

Show HN: Let ChatGPT write your reviews on Pull Requests https://ift.tt/A8HLEiC

Show HN: Let ChatGPT write your reviews on Pull Requests https://ift.tt/rqpHIxQ September 7, 2023 at 10:08AM

Show HN: Formstr: An open source and decentralized alternative to Google Forms https://ift.tt/QeYZjKg

Show HN: Formstr: An open source and decentralized alternative to Google Forms https://formstr.app September 7, 2023 at 08:54AM

Show HN: uDSV.js – A faster CSV parser https://ift.tt/NCXw9Ty

Show HN: uDSV.js – A faster CSV parser Hey folks! I know CSV parsers (especially in JS) aren't terribly exciting and someone writes a "better" one every week. I'm in the middle of my parental leave, and this was a project that came out of me looking for the fastest/smallest CSV parser. It all started so innocently, and then turned into a benchmark-validation-athon; the library itself took ~2 weeks to write, but the performance comparisons took another ~4 weeks (on and off). The benchmarks were a huge effort, but I think they are the most thorough to date, both in breadth and in depth, so hopefully you find them useful: https://ift.tt/lgAPyXv Let me know if you have specific concerns / questions / improvements :) cheers! Leon https://ift.tt/s3tlM6U September 4, 2023 at 09:34PM

Show HN: Nix Snapshotter – Native understanding of Nix packages for containerd https://ift.tt/pWB7Tjl

Show HN: Nix Snapshotter – Native understanding of Nix packages for containerd Hello! This is Edgar and Robbie and we built nix-snapshotter. nix-snapshotter brings native understanding of Nix packages to containerd. We built this because Nix is a great fit for making efficient containers. They don't need an OS because Nix captures all dependencies exactly. However, the current process of creating Nix images is subpar because one needs to transform Nix packages into a format that container runtimes understand. Using nix-snapshotter, instead of downloading image layers, packages come directly from the Nix store. Packages can be fetched from a binary cache or built on the fly if necessary. All existing non-Nix images continue to be supported, and Nix layers can be interleaved with normal layers. nix-snapshotter also provides a CRI image service, which allows Kubernetes to resolve image manifests from Nix directly too. This enables for the first time, fully declarative Kubernetes resources, all the way down to the image specification and its contents. With this, you can even run pure Nix images without a Docker Registry at all, if you wish. We'd love for you to try it out, there is a one-liner for Nix users to boot a VM with everything pre-configured: https://ift.tt/rz6FSXY https://ift.tt/rz6FSXY September 6, 2023 at 10:24PM

Show HN: ColorMood https://ift.tt/MG2qkc8

Show HN: ColorMood Does your mood affect which color you like - a tool that attempts to find your favourite color right now https://ift.tt/PDwCuix September 6, 2023 at 10:18AM

Show HN: Trellis – open-source Python framework to build DAG-based LLM workflows https://ift.tt/7YNz0Tx

Show HN: Trellis – open-source Python framework to build DAG-based LLM workflows Hey HN! Trellis is an open-source framework for programmatically orchestrating LLM workflows as Directed Acyclic Graphs (DAGs) in Python. My friend and I started working on this a few weeks ago after we tried building applications using mainstream LLM frameworks, and faced all the common complaints (too abstracted, hard to customize, bad docs/support). After talking to a few other people building with LLMs, we also noticed that these frameworks were not inherently built to support DAG-based LLM workflows. We designed Trellis to be as minimal and flat as possible, so developers can have lower level control over their DAGs. Trellis is composed of only three abstractions: Node, DAG, and LLM. Node: the atomic unit of Trellis. Nodes are chained together to form a DAG. Node is an abstract class with only one method required to implement. DAG: a directed acyclic graph of Nodes. It is the primary abstraction for orchestrating LLM workflows. When you add edges between Nodes, you can specify a transformation function to reuse Nodes and connect any two Nodes. Trellis verifies the data flowing between Nodes in a DAG to ensure the flow of data is validated. LLM: a wrapper around a large language model with simple catches for common OpenAI errors. Currently, the only provider that Trellis supports is OpenAI. Check out our docs if this sounds interesting: https://ift.tt/ojshWPK... We'd love it if you tried hacking with it and give us any feedback you have! :) https://ift.tt/fdkh47a September 6, 2023 at 07:34AM