I’m a cautious gadget freak. I love buying gadgets, but think a lot before buying them. Invariably, I use spreadsheets to help me decide. I try to buy only those gadgets that are right for me at the cheapest possible price, and I look at two things: features based on usage and breakeven. Usage-driven buying I pick the features I want based on my usage. For example, when I bought my first mobile, I listed the my most likely uses for the phone: ...
At what bitrate should you encode your MP3 files? Listening tests show that at 256kbps, you can’t tell the difference. But that’s with 2 amplifiers and big speakers. What about headphones? I tried an experiment with my cousin, who has the best ear for music that I know. We ripped a good audio CD of his at 128 kbps. He put on a pair of headphones (the kind that fit into your ear) connected to my laptop. I played the first half a minute of the original and the ripped version 10 times, in a random order, asking him to guess which was which. Result: 5 correct and 5 wrong. He couldn’t tell the difference. ...
Python vs Perl. Sums up my feelings perfectly: Python may be better for larger projects, but for my meddling, I’ll stick to Perl. It’s served me well for 10 years. Until 1999, I used Perl a fair bit, but no more than Java or C or anything else. My first “real-life” use of Perl was in 2000, when I was processing 600MB of IBES data. Access and SPSS couldn’t handle the load. Perl slurped all the data in a few seconds, though. A few years later, when processing bank data (3GB worth, this time), Perl again was the only saviour. In fact, between Excel and Perl (and CPAN), I think I have all the data analysis power I’ve ever needed. This blog, for instance, is written in an Excel spreadsheet, exported to XML, and converted into the blog format by Perl.
I have a large MP3 collection (Tamil and Hindi films). I don’t like selecting songs to listen to. Too much effort. I rated all songs I had listened to (650 songs x 5-10 seconds = 1-2 hrs) and created 7 SmartViews. I just go to one of these and play them in order. Here are my views, in descending order of their use. Most played. Sorted by Play Count. Songs I play the most. Plays stuff I listen to usually. Not heard recently. Played Last before 3 months ago AND Rating >= 3. Plays good songs I haven’t heard recently. Not played much or recently. Played Last before 1 month ago AND Play Count <= 2 AND Rating >= 3. Plays good songs I haven’t heard often enough. Recent hits. Last updated after 3 months ago AND Play count >= 3. Plays songs recently added and liked. Recently played. Sorted by Last Updated. Often, I like to listen to songs I listened to yesterday. Top rated. Sorted by Rating. My best songs. (Suprisingly, I don’t use this view much.) Recently added. Sorted by Played Last. Plays songs I just downloaded. But WinAmp’s not good enough. For example, I can’t find out what songs I played at least thrice last month. How do I see what I’ve been listening to a lot recently? Fortunately, there are a few WinAmp history plugins. I installed Pepper, which produces a log file that can be analysed. I did this two weeks ago, and don’t have enough data. When I do, I’ll modify two views ...
I don’t like hunting for new songs either. Too much effort. External recommendations like Raaga Top 10 help, but not much. I usually like only 1 of the top 10. I don’t really know the recent music directors. But many interesting songs I’ve heard recently (like Ondra Renda in Kakka Kakka, Vaseegara in Minnale, and Kaadhalikkum in Chellame) are by Harris Jayaraj. So maybe if I can find the music directors I like, other songs by them would be good recommendations. ...
I switched to Google Reader as my blog reader (I was using Mozilla so far). The reason was simple: speed. Thanks to the Google site’s speed and keyboard navigation, I can read blog entries 10 times faster. Now there’s a unique proposition for Google that a lot of people are missing: that their site loads a whole lot faster than others. It makes a huge difference to the whole browsing experience. ...
I have an automated (and lazy) way of finding interesting sites. This is what I do every day. I get the del.icio.us tags of every URL I blog about. (It’s available at http://del.icio.us/rss/url/ followed by the MD5 hex version of the URL). I pick the most popular tags (at least 50 links must have this tag), and use them as my “preferred tags” I scan the most popular sites on del.icio.us, and get each site’s tags If a site has my preferred tags, I give it points (the number of points is equal to the number of times I’ve blogged that tag) I pick the top 5 sites based on my points, and read them. There are two problems I have now. Firstly, I will find sites similar to those I have blogged about – not discover anything new. That’s fine to start with – I can search for those manually. The bigger problem is, this is restricted to del.icio.us. There are two ways I can extend this (lazily). ...