Hindi songs 2000s
Here is the background music from some Hindi songs from the 2000s. Can you guess which movie they are from?
Don’t worry about the spelling. Just spell it like it sounds, and the box will turn green.
Google search in Tamil
When I wrote my Tamil song lyrics quizzes, I had two problems:
- I can’t write in Tamil (not on paper, nor on a computer)
- I can’t spell right in Tamil (ந vs ன, ர vs ற)
I overcame the first using a Tamil transliterator. I write in English, and you see it in Tamil.
The problem of ந vs ன was simple. ந occurs as the first letter of a word, and just before த. Nowhere else. (Is this always true?)
But ர vs ற can’t be solved except through experience, and I’m short of that. So, rather than bother my family with every quiz, I used the wisdom of crowds. I googled both spellings of the word. The correct spelling has more Google hits than the incorrect one.
I did this so often, I made a Google gadget out of it.
Just type the word in English, click ‘Search’, and my gadget will search in tamil. It’s amazing how much stuff there is in Tamil on the Web, from song lyrics to texts (thirukkuraL, for example).
You can add this gadget to:
- your desktop (in the Search Gadgets box, type “http://www.s-anand.net/a/tamilsearchgadget.xml”)
- your website or blog (click here for the code)
- Google Reader.
Here’s the transliteration table:
| Tamil | English |
|---|---|
| அ | a |
| ஆ | A or aa |
| இ | i |
| ஈ | I or ee |
| உ | u |
| ஊ | U or oo |
| எ | e |
| ஏ | E |
| ஐ | ai |
| ஒ | o |
| ஓ | O |
| ஔ | au |
| க | k or g |
| ங | n |
| ச | ch or s |
| ஜ | j |
| ஞ | n |
| ட | t or d |
| ண | N |
| த | th or dh |
| ந | n |
| ப | p or b |
| ம | m |
| ய | y |
| ர | r |
| ல | l |
| வ | v |
| ழ | zh |
| ள | L |
| ற | R |
| ஷ | sh |
| ஸ | S |
| ஹ | h |
Google search in Tamil Read More »
Guy Kawasaki on The Art of the Start
Video of Guy Kawasaki’s talk on The Art of the Start at TiECon 2006.
It’s informative, even if you don’t want to start a venture, but I didn’t know Guy was such a funny speaker! He begins with:
Early in my career, I sat through many keynote speeches — at Comdex, at Mac Road Expo. I saw many many hi-tech CEOs speak, and I have to tell you, one thing I noticed is they pretty much sucked as speakers. And the second thing that I figured out sitting in these audiences of sucky keynotes is that if there’s anything that’s worse than a CEO who sucks as a speaker, it’s a CEO who sucks as a speaker and you have no idea how much longer he or she will suck! And so, I have adopted the top 10 format for all of my speeches. This way, if you think I suck, at least you can track progress through my speech.
Towards then end, when he’s run well over time…
What are you going to do? Not invite me again?
He gets dragged off the stage.
Guy Kawasaki on The Art of the Start Read More »
Infinite depth painting
Have a look at this infinite depth painting. You can zoom in forever. At some point, you realise, you’re back where you’re started. Almost like going around in circles, except that you’re zooming in.
Infinite depth painting Read More »
Google code search hacks
Jason Kottke finds interesting code search hacks, ranging from the WinZip key generation algorithm to programmers who want a new job.
Google code search hacks Read More »
Automated resume filtering
I had to screen resumes from a leading MBA school. I’m lazy, and there were hundreds of CVs. So after procrastinating until this morning, I decided on 2 principles:
- I will not spend more than 45 minutes on this. (That’s the duration of my train ride to office.)
- I will not read a single CV. (I would write a program.)
The CVs were in a single PDF file. I saved it as text (it shrunk from 66MB to 1.6MB without the photos). Then I wrote a Perl program to filter CVs by keywords. We were looking for people with an interest and/or experience in IT consulting, so I picked “technology”, “consulting”, “SAP”, “IBM”, “Accenture”, “Deloitte”, etc.
Anyone without these keywords would fall out of the list. This eliminated 75% of the crowd. But since I didn’t want to read the rest, I used my favourite text-analysis technique: concordance. I extracted 3 words on either side of each keywords, and just read those. It was easy to see who’d “worked with suppliers like IBM” as opposed to who’d worked at IBM.
That’s it! I managed to cut the list down to 10%. Better yet, I also had a preference ranking. People with multiple keywords ranked higher than those with fewer keywords. And all this took little more than my train ride to office.
I can see this going to the next level. It’s easy to write a customised rejection letter, depending on which keywords are missing for each person.
Now, if it’s this easy to filter resumes, I can see every organisation do it in a few years. Which means, you need to write resumes for machines as well, not just for humans! For example, on my next CV, I’ll make sure I include the words “Boston Consulting Group” as well as “BCG” — just in case the software searches for only one of those keywords. Further, I’ll make sure I avoid spelling mistakes!
Automated resume filtering Read More »
Netflix prize
Netflix has released a sample of its customers’ movie ratings at Netflix Prize. You can download these (700 MB), create an algorithm that rates the training data, run it against the test data, and see if you can get better ratings than their algorithm. If you do, you win $1 million. (Chris Anderson explains why.)
