poly

e, written in Rust. - https://github.com/EddieHubCommunity/LinkFree: Connecting with your audience with a single link, showcasing your content and projects. - https://github.com/FashionFreedom/Seamly2D: Design CAD to democratize and de-centralize fashion design & production. - https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow. - https://github.com/formbricks/formbricks: Open-source Qualtrics alternative - https://github.com/GyulyVGC/sniffnet: Cross-platform application to comfortably monitor and analyse network traffic - https://github.com/JessicaTegner/pypandoc: Pypandoc provides a thin wrapper for pandoc, a universal document converter. - https://github.com/mockoon/mockoon: Mockoon is the easiest and quickest way to design and run mock REST APIs. - https://github.com/nuxt/nuxt: An intuitive framework for building web applications, built for the edge. - https://github.com/responsively-org/responsively-app: A dev-tool for web developers that aid in faster responsive web page development. - https://github.com/simonw/datasette: An open source multi-tool for exploring and publishing data - https://github.com/spyder-ide/spyder: The scientific Python development environment - https://github.com/strawberry-graphql/strawberry: A GraphQL library for Python that leverages type annotations - https://github.com/termux/termux-app: A terminal emulator for Android that provides 2000+ linux packages to code on phone - https://github.com/TimothyStiles/poly: Poly is a fast, well tested Go package for engineering organisms. - https://github.com/trpc/trpc: End-to-end typesafe APIs made easy. Never write another API contract again. Edit: formatting and updated repo links

k traffic monitoring & analysis - https://github.com/GyulyVGC/sniffnet 11. Pypandoc: Thin wrapper for the universal document converter, Pandoc - https://pypi.org/project/pypandoc/ 12. Mockoon: Design and run mock REST APIs with ease - https://mockoon.com/ 13. Nuxt Framework: Intuitive framework for building web applications on the edge - https://nuxt.com/ 14. Responsively App: Accelerate responsive web page development - https://responsively.app/ 15. Datasette: Explore and publish data with this open-source multi-tool - https://datasette.io/ 16. Spyder: Scientific Python development environment - https://www.spyder-ide.org/ 17. Strawberry: Python GraphQL library leveraging type annotations - https://strawberry.rocks/ 18. Termux App: Terminal emulator for Android with 2000+ Linux packages - https://github.com/termux/termux-app 19. Poly: Fast Go package for engineering organisms - https://github.com/TimothyStiles/poly 20. tRPC: End-to-end typesafe APIs made easy - https://trpc.io/

IT and software engineers. It's what I call the naive buyers problem. The demand for software engineers in biotech is generated by naive buyers that don't know what they need, why they need it, or how to get it. Benchling and Recursion Pharmaceuticals have reputations in the industry of paying, "standard software salaries". So do the research divisions at places like deepmind/microsoft/google but in my experience there's even new multi-billion dollar institutes where senior management has never even heard the term devops. Most places advertise for "data scientist", positions or some analog, instead of software engineers. This is mostly because upper management has never met an actual practicing software engineer in a professional setting. Many come from academia where the culture and work requirements heavily disincentivize standard software engineering practices. It's also not uncommon for a biotech company to either have a very under qualified CTO whose main programming experience is what they learned doing ML research like stuff during their PhD or not even have one at all which has huge downstream consequences. This week a software engineer trying to make the switch to biotech actually DM'd me to ask why they were seeing a ton of data science / ML job positions but no software engineering / devops positions. They were worried that these companies were trying to save on costs by forcing their data scientists to create infrastructure but it's actually worse than that. Most of these companies aren't even aware that there's supposed to be infrastructure. Despite all of this the future is looking better and I'm starting to find new companies and positions that are well... reasonable. I learned about this thread from a friend at a party last night that works at one of these companies. There's a small, strong new wave of companies and developers out there pushing biotech software forward. Hopefully some (including myself) make it big while pushing the idea that better tech equals better biotech.

://github.com/TimothyStiles/poly). We're basically building (decent) open-source software for doing synthetic biology. Since you're a software developer, doing code reviews and reading our mega-comments (like https://github.com/TimothyStiles/poly/blob/prime/transformat...) might help you understand some more of the fundamental engineering problems we synthetic biologists are encountering. Also, in code reviews, if you don't understand something, a practicing synthetic biologist will explain it to you so that we can improve our docs.