any interesting tools are being pointed out by Jeff Veen and the latest such tool is the personalised tags cloud, which is currently in beta (testing) phase. This Web-based tool, which goes by the name TagCloud, inputs a collection of RSS feeds (as OPML) which reflect on your interests. It then counts the frequency of words within (essentially tags) while excluding stop words like “and” or “since”. Tags reflect on what happens in your own ‘cyber-sphere’ — the means by which you become better orientated. By looking at the cloud, you get a preview of what you’ll see plenty of once entering your browsing cycle.

Owing to his recommendation I built myself a cloud (seen above). The many news feeds make London the primary tag in my case. I later discovered a flaw: if a single feed contains a term which repeats itself over and over again, that term (tag) can dominate.

ahoo officially offer delivery of search results as RSS feeds. They join MSN Search in providing this type of service. Is it possible that Google’s competitors are willing to proliferate as much bandwidth as it takes just to challenge Google and steal some of its avid users? No doubt RSS feeds of this nature consume a lot of traffic and get a very low clickthrough rate from feed subscribers. In fact, people with interest in such a service are often pre-occupied with SEO and keep track of SERPs or news of one particular niche.

The news is rather disappointing for Google enthusiasts. As they offer no analogous service (Google Alerts instead), I set up about 5 feeds from MSN search (no clickthrough though, ever), 2 from Yahoo! Finance (since February 2005) and 5 from Yahoo! News. Google appear reluctant to provide feeds so I ended up using a workaround. There are similar workarounds for eBay or even static HTML pages.

n a life that involves subscription to many feeds, it is worth knowing which ones are the most popular. The Radio Community Server collects some statistics and ranks the Top 100 RSS feeds based on the number of subscribers. Below is the top of the list:

All the rest can be found at the aforementioned site.

Feeds slowly become the substitute to the Web browser

Caution Firefox enthusiasts

here is an alpha version of Firefox, codenamed Deer Park, which is used to test future releases of the Mozilla browser. The following page explains a little further about the purpose of the package. My advice is to avoid the use of Deer Park as it interacts badly with your Firefox profile and can conflict with your current settings, extensions and themes in particular. The Deer Park page clearly states:

Deer Park Alpha 1 is intended for web application developers and our testing community. Current users of Mozilla Firefox 1.0.x should not use Deer Park Alpha 1.

odern browsers are among the more complex pieces of software in existence. Because browsers are expected to treat similar data (the World Wide Web) and behave consistently, there is plenty of room for bugs to crop up. Big trouble lies ahead if a browser is re-released infrequently or no updates are made available, apart from the critical. This is probably the main catalyst to the “Don’t click on the blue E” campaign.

Web developers spend extra time trying to compensate for Internet Explorer bugs. In css-discuss, for example, almost half of all questions (if not more) concern browser compatibility, fixes and hacks. Internet Explorer is often the culprit. Rather than fixing/hacking around IE bugs, perhaps we should all upgrade to a browser that is actively maintained and released to the public. There is no doubt as to how fed up development communities have become with IE, which for most users ships by default.

ZDNet report that Google will release an official Google toolbar for Firefox.

Google is poised to release a version of its toolbar for the Firefox browser, according to information sent to developers of an open source toolbar alternative.

…

It has pretty much the same features as the latest IE toolbar except of course for things like the popup blocker

Various other bars (using the same API) have indicated erratic PageRank recently and PageRank was sometimes greyed out for no known reason (see below). About a month ago it was greyed out globally for 2 or 3 days straight.

Until google release their own homebred bar for Firefox, the GoogleBar project will serve as a primary alternative. However, PRGoogleBar (formerly the PageRank bar) is much more powerful as it experimentally makes use of Google Suggest for search phrase completion. It also incorporates extra shortcuts and the ability to customise SERPs behaviour. This bar is definitely better than the equivalent from Google.

Another excellent search-related bar is SearchStatus, which additionally provides Alexa ranks and does not occupy much screen space (see below).

SearchStatus in action

Finally, also worth mentioning is Yahoo’s search bar which has been kind to Firefox for quite some time.

ow does one identify music which has potential of being liked? Music, unlike textbooks, does not contain text or keywords. Its tags are not always valuable either. An interesting paper from Trinity College Dublin describes a method by which music adapts to the preferences of listeners (PDF). However, can this be done purely based on prior data? Data that is provided in advance unlike in real-time? A List of records maybe? Playlists perhaps? We seem to be coming closer to realisation of this idea.

Image similarity measures are one focus point of my research; also sparks to mind is Google’s notion of ‘Similar Pages’. Why not apply similar principles to music? I now collect big daily dumps of music that I listen to (output to files using the following technique ). Bound to each entry is the time when a track started. From this, one can infer which tracks are being skipped. Alternatively, full, raw playlists can be of use and might, in fact, be more manageable as well. By exploiting a large collection of playlists, the nature of the genres can be better understood.

Given all of this data, it can potentially be used for collabortive playlist sharing, somewhat like del.icio.us (see previous reference to del.icio.us with a gentle introduction). Users can then discover other songs they might like based on other people’s playlists. The more data, the more accurate statistics will be. Getting large lumps of input (playlists) is effortless too. Just imagine yourself the scenario:

You can automatically find playlists most similar to yours and recognise the most-played tracks on that playlist. Social software has seen great success recently, so exchange of music preferences and recommendations is probably the way to proceed.