herrherr

As your products and services are targeted towards bigger clients, do you have any advice for smaller companies on who to talk to regarding pricing?

Any hints on where to start?

First of all, thanks for open sourcing lmdb :)

The biggest problem currently is actually degrading performance, although I'm almost 100% sure that this isn't caused by lmdb itself, but rather by the bindings I've tried.

In the end, doing it directly in C is probably the only thing that will actually work.

Actually I'm also using lmdb (together with Python/numpy) :) Added an email address to my profile, would be happy to exchange some experiences.

I've been trying for weeks now to get a system running that can handle larger than RAM datasets and returns queries in an acceptable time. It's running ok now but far from optimal (size of DB is ~100 GB and it contains a few hundred million entries).

Does anyone here have experience with any implementations (such as likelike, lshkit, etc.) and can recommend something that can handle larger sets? All the implementations I have found were either not maintained, old, not running or not suitable for production use.

Will definitely take a look at the paper but unfortunately it's always a very long way from here to an actual implementation (there is no code published as far as I could see).

Currently without a doubt: https://www.youtube.com/user/mathematicalmonk

An extensive series about machine learning (100+ videos).

I think you are on the right track there.

The thing is though, you won't have difficulties finding papers on those topics. However, you will probably not have any luck finding many concrete and practical implementations that you could look at.

So it's a far way from reading the papers to having something working.

If you find something, please let me know.

We are currently using perceptual hashes (e.g. phash.org) to do hundreds of thousands of image comparisons per day.

As mentioned in another comment, you really have to test different hashing algorithms to find one that suits your needs best. In general though, I think it is in most cases not necessary to develop an algorithm from scratch :)

For us, the much more challenging part was/is to develop a system that can find similar images quickly. If you are interested in things like that have a look at VP/MVP data structures (e.g. http://pnylab.com/pny/papers/vptree/vptree/, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.7...).

Per se the product looks interesting, but having all these new accounts praise it, looks a bit odd.

Shameless self-plug:

www.getmetricmail.com :D

The really interesting part is actually to recognise how hard it is for a new app to enter my daily-use list. It's almost impossible. Some make it in there for a few days or weeks but will vanish quite soon.

Either I need the app for my daily work or it is a fire-and-forget service that I once signed up for and that doesn't require any active input from my site.

Anyway, here is my list:

- pivotaltracker.com

- github.com

- dropbox.com

- olark.com

- gmail.com

- google.com/analytics

- hipchat.com

For comparison have a look at the getclicky stats: http://getclicky.com/marketshare/global/web-browsers/

They seem to be pretty close.

Indeed. Google Analytics allows you to create those reports. But apparently it's far too difficult for people who are not that tech-savvy.

As I said we currently have over 3000 users, so there seems to be some interest in such a solution :)

You can receive pdf attachments directly, so you don't have to click on the link. Nevertheless that's not what you're looking for, I guess ;)

We thought about putting the data directly into an email, but the crappy HTML/CSS support in the gazillion email clients, make this a pretty tough job.

That is actually possible.

The case here shows what happens when you offer too much features/resouces in the free plan.

The toughest part now is deciding if it makes sense to invest more time into it. But I guess that is a general problem for startups that haven't yet found product/market fit. You can't really know if you are miles or just an inch away from that fit.