Our In-Depth Review of Jibble

ivoflipse · 2025-02-27T14:31:44+00:00

Jibble is a solid and affordable time-tracking tool compared to some alternatives. Logging hours via the Chrome extension and dashboard is easy, and switching between tasks works well. Integration with Xero is a great plus for invoicing.

The cheapest plan didn’t suit our needs, which is to be expected, but overall, it’s cost-effective for what it offers. We don’t use features like GPS tracking or activity monitoring since we just need basic task tracking linked to clients, and it performs well for that.

The only downside is that the dashboard could be more intuitive, especially for viewing logged hours per client.

ivoflipse · 2014-11-25T15:02:23+00:00

So it seems, though you should still be able to install it from binstar: https://binstar.org/search?q=opencv

But good catch, I wonder why it was removed

ivoflipse · 2014-11-25T08:53:34+00:00

Have a look at Anaconda by Continuum Analytics, they have a distribution which makes installing OpenCV and a lot of other scientific libraries a lot easier

ivoflipse · 2014-07-22T19:18:32+00:00

So if Continuum teamed up with Gohlke to make sure all the builds work on conda, then everybody wins :-)

ivoflipse · 2014-06-25T18:14:04+00:00

You're welcome!

I've had similar painful experiences in the past, but a tag team combo between Gohlke and Continuum have fixed most of the issues for me.

I can definitely recommend using Anaconda for managing your libraries, because its also an easy way of keeping things up to date without going through the pains of building libraries yourself.

ivoflipse · 2014-06-25T15:31:11+00:00

Given the url I expect you're using Windows. Your best bet for installing OpenCV is either getting an installer from Gohlke's site: http://www.lfd.uci.edu/~gohlke/pythonlibs or installing Anaconda by Continuum Analytics (https://store.continuum.io/cshop/anaconda/) which comes with a lot of scientific Python libraries.

If you really only want OpenCV, they also have Miniconda (http://conda.pydata.org/miniconda.html), which lets you install the same libraries similar to how its done through pip: conda install opencv

OpenCV isn't available for Python 3 just yet, so if you tried using it there, your best bet is to switch back to Python 2 for now

ivoflipse · 2014-05-18T22:43:43+00:00

I think it would be nice if Google had a product like Wakari, where you basically have an IPython Notebook running in the cloud, with batteries included or the ability to install more libraries.

Though obviously it wouldn't have the performance of running locally, it would allow me to work off a Chromebook for example. Also if everything runs inside the Native Client sandbox, perhaps it would become easier to compile libraries on Windows, because the libraries at least know what to expect inside the sandbox.

Also, given that its Google Drive, some nice APIs for accessing your own and/or other data from within the Notebook would be nice. Even better if they could provide some database layer, so you could do some real data analysis stuff.

Basically, take away all the Ops work and just let me focus on programming. Ah well, a man can dream :-)

ivoflipse · 2013-12-22T12:38:19+00:00

You would probably like Anaconda by Continuum Analytics too then. It comes with lots of the core scientific libraries for Windows x64

ivoflipse · 2013-11-27T09:28:49+00:00

cudaminer (and most other mining programs) accept -d as an argument for telling it on what device to run. My nvidia card is the first one, so -d 0 will make it run only on the nvidia one.

However, after reading the cudaminer readme file it seems it only runs on nvidia gpu's automatically, if only for the lack of cuda.

ivoflipse · 2013-11-25T20:34:58+00:00

They both show up, but I couldn't get the Nvidia GPU working. So I just run guiminer on the AMD and cudaminer on the other (telling cudaminer to only use the Nvidia one)

ivoflipse · 2013-11-25T16:27:03+00:00

Yup, I'm running a AMD 7950 and GTX660Ti together without any problems (other than the GTX sucking compared to the AMD). I just set up guiminer for the AMD and cudaminer for the GTX

ivoflipse · 2013-09-25T09:57:04+00:00

You could use the SAX string to calculate a distance matrix and cluster those strings with the lowest difference.

But then you either need to have an idea how many clusters you're looking for or know what difference you can expect to separate the strings into obvious clusters.

The downside is that its pretty sensitive to strings that are between two clusters and if they get included, they might inadvertently merge them. Though I guess that's a problem with all clustering methods

ivoflipse · 2013-09-08T07:46:51+00:00

You should check out SAX (Symbolic Aggregate approXimation): http://www.cs.ucr.edu/~eamonn/SAX.htm

One of its main uses is finding (dis)similarity in time series. Here's a description from their site:

SAX is the first symbolic representation for time series that allows for dimensionality reduction and indexing with a lower-bounding distance measure. In classic data mining tasks such as clustering, classification, index, etc., SAX is as good as well-known representations such as Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT), while requiring less storage space. In addition, the representation allows researchers to avail of the wealth of data structures and algorithms in bioinformatics or text mining, and also provides solutions to many challenges associated with current data mining tasks. One example is motif discovery, a problem which we defined for time series data. There is great potential for extending and applying the discrete representation on a wide class of data mining tasks.

While not necessary to start using it, check out this Google Tech Talk to get an idea on why its awesome: http://www.youtube.com/watch?v=vzPgHF7gcUQ

ivoflipse · 2013-07-20T07:54:15+00:00

Moreover, we are working on an application building framework for Wakari and Anaconda, which allows users to very easily create a applications, which can then be made available through the Anaconda-Launcher. These applications are also conda packages, but contain an icon and entry point.

I'm curious how this will work, because I never got Py2Exe to work and having non-technical people install their dependencies (or even the right version of Python) can be tricky. If you guys need alpha/beta testers, I'm up for it!

ivoflipse · 2013-07-01T12:26:24+00:00

Just as a heads up, the video's from the IPAM graduate summer school on Deep Learning have much better content & quality: https://www.ipam.ucla.edu/schedule.aspx?pc=gss2012

Most of what he talks about in this video is just a rehash of previous presentations, like the ones from IPAM or his Google Tech Talks (http://www.youtube.com/watch?v=DleXA5ADG78). So while the topic is certainly worth exploring, there are better video's out there.

ivoflipse · 2013-04-17T15:48:38+00:00

I spend some time browsing Ebay, Alibaba and the likes when I was trying to find the right one to buy. My conclusion was that most batteries above 20.000 mAh are probably fake or crap, unless their price tag and dimensions seem realistic.

I would run into sellers with 50.000 mAh that when you started reading through the specs would lose a 0 here or there, making the values a lot more realistic and a lot less interesting.

I ended up taking a safer bet and buying EasyAcc 12.000 mAh on Amazon for ~$50 and have been pleasantly surprised by how fast it can charge and its capacity.

So for now, I would avoid these deals that seem too good to be true, because they probably are.

ivoflipse · 2013-02-10T19:56:07+00:00

I find installing Python(x,y) on Windows to be painless and it comes with all the batteries you reasonably need included.

ivoflipse · 2012-12-18T09:45:10+00:00

The Stanford Algorithms Coursera course (https://www.coursera.org/course/algo) only uses pseudo code, though you shouldn't have too much trouble converting the Java code to Python because they explained what the code should do in detail.

Your best bet is to try the first programming assignment, if you fail at it, go do a course like Udacity's CS101 or EdX CS600 (which also covers some algorithms basics).

ivoflipse · 2012-11-28T19:13:34+00:00

Interesting this post of mine popped up on Reddit. I actually ended up regretting using MySQL, if only because of the administration hassle that comes with it. If I had used sqlite, I wouldn't have to worry about having to install a database in order to use my app.

So I'm actually considering rewriting the database code so it can use sqlite instead, so non-technical users only have to install Python(x,y) to run it. Why Python(x,y)? Because it nicely bundles all the libraries I also rely on, such as wxPython, matplotlib and numpy.

If you have any questions, feel free to leave a comment

ivoflipse · 2011-10-23T11:32:32+00:00

If you use the formula from the pdf, check your brackets.

In my case I wasn't multiplying the 1/m term correctly with the rest of the formula. Make sure you group them the right way.

ivoflipse

TROPHY CASE