I'd love to learn more about the pre-screening process. Does it mean I need to create another profile with you or the idea behind is something different.
@bogomep Hi Bogo- we're working on keeping the talent profile process as short as possible. A PDF parser is in the works.
Right now, it basically takes about 1 min to fill up your profile (github URL, whether you are willing to relocate, etc). TARA then works on analyzing your code in existing repos (5-factor analysis that includes compile code check, your overall commits, code complexity) and gets to work on recommending you to companies in our network. Gradberry has made just over 700 recommendations to 255 companies in the last 20 days.
Hey guys, founder here. We’re excited to announce version 2, with our new recruiter AI named T.A.R.A (Talent Acquisition and Recruiting Automation). We’re now working with 200+ startups and SMEs (including Dropbox, Udacity and JWPlayer) in Bay Area and New York, to curate code-verified talent.
Getting hired shouldn’t be about how impressive your school is or how well you interview—it should be about the quality of your code. That’s why we set out to build gradberry: a better way to connect technical talent to the best companies.
In a nutshell: our AI, TARA, analyzes a developer's code (from existing repos via github, bitbucket, etc) and recommends your profile to companies who need your unique abilities and skills.
Love to hear your questions, we'll be here all day :)
@ibamasood Definitely a team of Star Wars fans, enjoyed the easter eggs in the sign up form…
Would really love a service like this that I could also submit applicants that I've found and get your feedback on their quality of coding skills. Definitely be good to have my team look over it too, but good to have an outside opinion as well.
That means this application is only useful for tech people? Even among tech pool, not everybody is contributing to OpenSource or Github. Are you seeing any issues with traction? It seems very limiting. Care to answer?
@sridhar_kondoji Just over 255 companies and 3,000 new engineers joining the pool (no such issues with traction, recruiting is a nightmare for startups in Bay Area and beyond). When you launch a startup, it's always best to start with a niche, conquer it and move to other verticals accordingly. Data can be analyzed in other fields as well, and machine learning can be applied to different sets of candidates for curation that mimics a human recruiter changing each set based on feedback from the employer.
P.S. More info here: http://techcrunch.com/2015/05/27...
@ibamasood@sridhar_kondoji Are you using some static/dynamic code analysis tools and code profiling techniques to get the data?
These analysers can only be relied upon to certain extent and after that manual intervention is needed to get the perfect answers. Again, my concern is good candidates should not get burned because of the algorithms. Good luck.
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