Joined the Giffgaff Affiliate Program

K Ring Technologies Ltd., can benefit from you switching your mobile telecommunications provider. I, the director, have become not wanting to use my current provider, and have seen that Giffgaff offer controls to prevent premium rate service scams (an option in settings), and offer other good data bundles. Simply order a SIM via the top right link on the page.

After using up the 6GB 4G full speed allowance, the slower speed is sufficient for audio streaming, but video streaming depends on the quality settings, and often needs pausing to stream up the buffer. This is fine for most use, and the connection goes full 4G after midnight until around 7 o’clock. This is good for unsupervised downloads. All in all, looking like an excellent service compared to three, although slightly slower, and has a much better internet interface avoiding the extremely bad three customer service. I mean how many times does one have to ask? The web interface at giffgaff allows via settings to disable all premium rate, to avoid call back scams and such. The community forum is also good.

Now to obtain a PAC code from three. Wish me luck.

Been using it for some days now, and the first problem is a text to kill the always on data. It turns out that they do not perform any dynamic rate limiting to keep you within the 256 kb/s limit after your 6 GB has been used. This is strange and maybe relates to them subcontracting a carrier network. I’m testing out a Windows program NetLimiter to see how that goes.

NetLimiter has a nice feature to schedule rules on band limiting at the peak hours of the giffgaff account, so a minute before, and one minute after, the limit engages. It also has some nice firewall features. It was on beta test offer for registered users, and so for me it was free.

Latest CODEC Source GPL v2+

The Latest compression CODEC source. Issued GPL v2 or greater. The context can be extended beyond 4 bits if you have enough memory and data to 8 bits easily, and a sub context can be made by nesting another BWT within each context block, for a massive 16 bit context, and a spectacular 28 bit dictionary of 268,435,456 entries. The skip code on the count table assists in data reduction, to make excellent use of such a large dictionary possible.

The minor 4 bits per symbol implicit context, has maximum utility on small dictionary entries, but the extra 16 times the number of entries allows larger entries in the same coding space. With a full 16 bit context enabled, the coding would allow over 50% dictionary symbol compression, and a much larger set of dictionary entries. The skip coding on large data sets is expected to be less than a 3% loss. With only a 4 bit context, a 25% symbol gain is expected.

On English text at about 2.1 bits per letter, almost 2 extra letters per symbol is an expected coding. So with a 12 bit index, a 25% gain is expected, plus a little for using BWT context, but a minor loss likely writes this off. The estimate then is close to optimal.

Further investigation into an auto built dictionary based on letter group statistics, and generation of entry to value mapping algorithmicaly may be an effective method of reducing the space requirements of the dictionary.