Papers with Code
ML5.js
arXiv
Spell
Lobe
ML Showcase
Apple Machine Learning Journal
Amazon Machine Learning
Workable
Greenhouse
Breezy.hr
Lever
Recruitee
LinkedIn Recruiter
Jobvite
SmartRecruiters
Workable is affordable, useable hiring software. It replaces email and spreadsheets with an applicant tracking system that your team will actually enjoy using.
Papers with Code WorkableWe used Workable to manage hiring across a few open roles, and overall it made the process much more organized than juggling emails and spreadsheets. Posting jobs and tracking candidates in one dashboard helped keep everyone on the same page, especially when multiple people were involved in interviews and feedback.
Where Workable shines is simplicity. You donโt need much training to get started, and most features are easy to understand. That said, if your hiring process is complex or heavily customized, you might start to feel boxed in. Some advanced reporting and automation options are also locked behind more expensive plans, which may not feel worth it for smaller teams.
Overall, Workable is a reliable, well-designed hiring tool that does exactly what it promises. Itโs not perfect, but for teams that want a clean and efficient recruiting setup without too much complexity, itโs a solid choice
Based on our record, Papers with Code seems to be a lot more popular than Workable. While we know about 100 links to Papers with Code, we've tracked only 1 mention of Workable. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Benchmark Primary focus Evaluation metrics System coverage Usability Link HumaneBench AI benchmark Human well being, humane AI principles HumaneScore, flip tests under adversarial instruction, long term well being 15 popular chat models tested across 800 realistic scenarios Designed for chatbot safety research; requires ensemble judging for... - Source: dev.to / 7 months ago
An helpful approach is to browse the state of the art models in paperswithcode. This will give you an idea of the performance of different models on various tasks. - Source: dev.to / almost 2 years ago
I think a way around this would some sort of voting/ popularity system? Papers with code (https://paperswithcode.com/) does this via Github stars sorting. Sure it doesn't mean something is established. But it at least gives some way to filter through the firehose of papers. Love this project btw! I think it has potential (and the timing is right now that everyone is looking for the next "attention is all... - Source: Hacker News / almost 2 years ago
Adapting to Evolving Standards: With the rapid progress in deep learning research and applications, staying current with the latest developments is crucial. The checklist underscores the importance of considering established standard architectures and leveraging current state-of-the-art (SOTA) resources, like paperswithcode.com, to guide project decisions. This dynamic approach ensures that projects benefit from... - Source: dev.to / about 2 years ago
Papers With Code is one of the good resources to get you to get started. - Source: dev.to / about 2 years ago
Link is 100% safe for work (it's a 3rd party job listing site - workable.com). Source: over 3 years ago
ML5.js - Friendly machine learning for the web
Greenhouse - Greenhouse Software makes companies great at hiring.
arXiv - arXiv is a free distribution service and an open-access archive for scholarly articles.
Breezy.hr - A Modern Hiring Tool for the Entire Team. A uniquely simple, visual hiring tool you and your team will love.
Spell - Deep Learning and AI accessible to everyone
Lever - A modern web app for hiring. Lever is a simple, powerful way to manage lists of candidates during the hiring process.
