In an effort to simplify my life, I’m consolidating various bits of my online personality. You can now read about S7 Labs, technology, business, and other musings on my personal blog, It Burns! It Burns!
“In this series, reporter Alison Freeland collects the stories, challenges, and goals of six businesses, and then goes to Yale professors Barry Nalebuff and Ian Ayres, who brainstorm on what these businesses can do to reach those goals- and we learn something about the economy and business.”
If you’ve used easy_install and setup.py before, you may have noticed that running something like “easy_install -d PATH” versus “python setup.py install PATH” produce different directory hierarchies. If your development environment consists of both packages pulled from the Cheeseshop and your own code (or even your modifications on Cheeseshop hosted packages), then you need a way to put packages from both installation methods into the same path.
It turns out that easy_install can take a tar.gz file as an argument. So, you can take a package foo that has a setup.py defined, create a foo.tar.gz of the directory containing setup.py and all the associated code, then run “easy_install -d PATH foo.tar.gz”, which will create the egg and install it for you in the proper way, alongside your Cheeseshop pulled packages.
I’m skeptical that a Bing-News Corp deal would actually be beneficial for News Corp. I think the Google brand, when searching for general news, is stronger than the actual news source.
For example, if I want to know about Michael Jackson, I search “Michael Jackson”, and to a degree I don’t care what source gives me what I want to know. However, if I want to search for an article in the WSJ, I go to the WSJ site and search for it there. The first kind of traffic is beneficial to News Corp via Google, and that’s what they would lose if they struck a deal with Bing. The second kind of traffic, they were going to get anyway.
Ever since I read about the pay-what-you-want-for-bagels model in the Freakonomics book, I’ve been curious whether or not I might be able to apply it to other transactions. I’m glad to see someone testing the waters. About 40% paid all they could afford, or paid specifically because of the model, which suggests that a large percent of these sales would not have occurred had the model not been in place.
There’s a little bit of misconception running around about pre-existing conditions and the evils of insurance companies. The HIPAA link explains that when you switch insurance carriers, the new one can’t exclude pre-existing conditions. The key here is that you can’t let your insurance coverage lapse; if you’re laid off or you quit to work on a startup, you absolutely must make sure that you’re on Cobra or perhaps a low cost high deductible plan.
If you do let your insurance lapse, then the next insurance company can only exclude covering the cost of pre-existing conditions for the next 12 months at most (less than that, if you had any continuous coverage the previous year — but see the HIPAA link for details).
Why am I blogging about this now? Because Cobra coverage from my previous company is running out, and since startups often times aren’t cash flow positive, they need to understand the details of how insurance coverage works. My situation is compounded by the fact that I’m also doing no-charge freelancing work for some non-profits and groups that I like, and so it’d even be a stretch for me to qualify for Freelancers Union insurance.
Larry Summers mocked efficient market theories, saying that they’re as insightful as finding that two quart bottles of ketchup are twice as expensive as one quart bottles. Relative valuation seems the same deal to me.
We’ve officially released CyLucene, a Python interface to CLucene. CLucene is a C++ implementation of Apache Lucene, the popular search engine library.
Python developers wishing to use a well supported search engine typically head to PyLucene. PyLucene’s approach is to build an interface to a C++ layer using their custom grown JCC compiler, which then talks to Java Lucene via JNI. That’s two layers of indirection, one of which is JNI.
Even if you believe JNI is efficient, there’s still the added overhead of requiring Java at runtime. If you run a website on a shared hosting service that restricts you to, say, 80 MB of RAM, CyLucene might be for you.
CyLucene 0.1 is minimally useful. It good enough to power Standing Room’s upcoming search features, but it’s far from a complete CLucene bridge. We’re in real need of feedback as to what features we need to expose to make the package more generally useful. You can find contact info in the readme.txt file, and can file a bug as always.
