Mahalo, Part II

A few months ago I covered the launch of Mahalo, a new “people-powered” search engine which aims to make human-crafted, intelligent portals for the most common search terms (example). I explained the search engine as “based on the theory that many people are searching for the same things, that search engine spam is making Google less useful for common queries, and that humans are still wiser than algorithms at sifting through results and finding the really good stuff.” I also noted that I found the real strength of Mahalo to be that the people crafting these portal pages could not only find the best links, but also give really great, human-crafted link context and tell the story about “what you’ll find there and why you should take the page seriously” much better than Google can.

I still stand behind all of that. And I still think Mahalo is a very cool project.

However, I also believe Mahalo is staring down the barrel of two pressing questions:

1. How can Mahalo, a human-powered search engine, get on the correct side of AI/algorithm progress? While Google’s algorithms are only going to become better, Mahalo’s portal system is fairly ‘fixed’ in its structure and doesn’t appear to have that same sort of potential to benefit from progressive tweaks in the code. Aggregated over 10,000+ results and several years, this promises to become very significant.

2. How can Mahalo break into the local search scene? Google’s doing cool things with local search, but Mahalo’s search results are not local, and are currently not structured such that local results can easily sneak their way in (as with Google).

In what I’d deem a blogsperiment, I’m going to offer possible answers to these two questions *if* somebody puts some money in my tip jar and earmarks it ‘Mahalo’ (Calacanis, I’m looking at you, though this isn’t a shakedown- just a value trade. I figure you of all people will appreciate the incentive structure). Your choice of whether I blog it or email it.