Ask HN: Need feedback on my Professional Quantification website, Predikt.co
Hello hackers, I am looking for feedback on Predikt.co (Quantifies your Linkedin and Facebook profiles). 80+ users have already registered and we are in a beta mode. What do you think of this idea? Here is a sample profile if you dont want to log in (http://predikt.co/users/view/6427DX551) or view trending users www.predikt.co
6 comments
I think the idea has potential. I am curious what kind of insights and analytics you will be providing.
It would be good to start with a specific audience (e.g. programmers) and use GitHub activity to factor in their professional experience as an example. I can see this being useful for recruiters to screen developer linkedin profiles.
The name predikt implies you plan on predicting something, but I am unclear what you are predicting. Potential salary range? How likely to leave current job? etc.
It would be good to start with a specific audience (e.g. programmers) and use GitHub activity to factor in their professional experience as an example. I can see this being useful for recruiters to screen developer linkedin profiles.
The name predikt implies you plan on predicting something, but I am unclear what you are predicting. Potential salary range? How likely to leave current job? etc.
Hi, thanks for the comment. There is some ongoing development on predicting couple of main things:
1) Would a particular person be potentially a good candidate for certain role. This is done by using similar other profiles as a training set and use those factors to predict early on if someone would be a fit. e.g. People who have studied in a university of reputation X, have Y yrs of experience and have Z skills go on and achieve position 'A'. These factors would be used to predict the users who share the traits and would likely land in a similar position.
2) Predikt the probability of job hopping, likelyhood of someone being a passive job seeker, potential salary ranges the candidate falls into.
Right now, Linkedin and facebook integration are functional, certainly it would be more useful to add all possible networks. Github, Stackoverflow would be next. Some recruiters have approached and would like to use this for screening their applicants.
Right now, Linkedin and facebook integration are functional, certainly it would be more useful to add all possible networks. Github, Stackoverflow would be next. Some recruiters have approached and would like to use this for screening their applicants.
Exciting! Thanks for the additional info, I definitely have a better picture now.
I think the recruiting industry can definitely use more help from technology.
I think the recruiting industry can definitely use more help from technology.
Thanks. Here is the brochure for recruiting solutions, it has some more details: http://bit.ly/predrecsol2
Add comment for clickable links:
Linkedin or Facebook login: http://predikt.co/
Sample profile: http://predikt.co/users/view/6427DX551
Trending users: http://predikt.co/users
Neat idea. I have to ask how FB comes into it. As most educational and work info is going to be on Linkedin?
Thanks bruce. Yes, Linkedin is for sure the professional destination. Users are slightly hesitant to give out linkedin data, Facebook has a 5x larger userbase and people tend to fill minimal data such as job title and education, so the idea is to build and use a professional graph so as to interpret relevant information to the data and quantify it. There is some useful info such as interests, likes, groups etc which give vital information too.
The plan is to integrate all possible social sites and aggregate the professional data out of it.
- Clickable Link ~> http://predikt.co/
Personally I like the idea. I have never heard of it before. Something like a Credit Score for profiles.
Personally I like the idea. I have never heard of it before. Something like a Credit Score for profiles.
Special thanks for mentioning Credit Score. The algorithm we are building is pretty much like a Credit Score algo. Not just because we show a score for users, but because we use factors learned and deduced from similar other profiles and use it to quantitatively predict the likelihood of a fit (risk in case of a credit score). It would be ideal to use larger datasets for training, however its still in beta and the algorithm is being updated continuously.
Thanks for the clickable link too :)