You might find Portable Native Client interesting, which is built out of LLVM+NaCl.
http://www.chromium.org/native...

I'm hoping that eventually browsers, mobile phones, and cloud hosting will just become sandboxed LLVM targets. Then people could use whatever language they like, wherever they want.

I live near Google and also work there. The bus stops are not anywhere near campus, as they are only intended for people with long commutes where pooling makes sense. I live 7 miles from my building, which is bikable and drivable, so no bus stop or bus for me. What you see around campus is empty buses during the day because they need somewhere to hang out between the morning and evening rush. I think you might be misinterpreting this as how they always look.

They are not driving them empty up and down the peninsula all day. Sometimes during rush hour there is one direction that'll be almost empty, but in those cases there are a *lot* of people the other way.

Many-to-one transportation systems (like corporate shuttles) are much easier to run efficiently than many-to-many systems (like most city buses). This is why systems use hubs -- even though they take you well out of your way, they are cheaper for the carrier. But in the case of a shuttle, the hub *is* where you want to go, so there is no loss of efficiency.

Manhattan has 1.6M people at an average density of 70,500 per sq mile. I don't know of any 1 Gbps service for $25 there.

How about where a company sinks several person-years of effort into a library or software suite, then decides to open source it? If an external developer spends a few days fixing a bug, it would be nice if the company that footed most of the development cost could use that fix in their internal deployments. Only fair, right?

Now hopefully it makes sense why companies release mostly BSD software (Clang being a good example). It is also why developers BSD things that they want companies to embrace (basic components where wide use helps like zlib, or things where you'd like a company to foot the long-term cost).

I've released both GPLv2 and MIT-licensed libraries. My current employer allows me to open source some work, but strongly encourages Apache licensing. I follow that, because they paid me to write it and it seems unfair to limit their future use.

Use quotes around words, or enable "verbatim results" in the options. The + thing was misunderstood and misused by most users, and they figured experts could RTFM.

The thesis you are basing your work is from 1977; while no doubt current when it was written, there is has been a lot of work on human signal decoding since then.

I'd strongly suggest looking at Hidden Markov Models:
    http://en.wikipedia.org/wiki/Hidden_Markov_model
While some recent methods have gone beyond HMMs for speech recognition, that's been the baseline "good" solution for the past decade.

Since this is a binary signal problem another approach to consider would be Markov Random Fields (MRFs) which could be used as an initial de-noising pass or even as a full decoder if you set the cost functions right.

Your idea of user adaptation is pretty reasonable, but my guess is the primary thing that matters would be an overall speed scaling. IOW for good decoding you probably just need to normalize the average letter rate between users.

Good luck.

For public stop usage the SFMTA was aware of and already working toward a solution since late 2011:
    https://www.sfmta.com/projects-planning/projects/shuttle-partners-program/detail
It's all laid out pretty reasonably without having to get into a ticketing war or protests. These protesters are late to this issue, yet will probably claim credit when the mutli-year regulation update goes in place next year.

Of course, this is just a side issue for the bus protesters, it is more about the evictions. There are a lot of things driving that from zoning regulations to economics, so they pick a visible if somewhat poorly representative target.

In your case, it does seems like quite a traffic growth problem, but replacing the each bus with 30+ cars doesn't seem like a good solution.

The Goobuntu part is incorrect. Both my laptop and desktop have GCC 4.6.3 installed right now.

It is exceedingly unlikely that the results don't overlap after the first few, but if you can produce a copy of the two sets of results, I will forward them to someone on the Google Search team for debugging.

People hugely overestimate the effect of personalization -- it is a ranking tweak not a complete change to the search engine. It does not make economic sense to have personalized whole-web indexes.

Btw, if you don't like personalization ever, it is pretty easy to turn off:
    https://support.google.com/accounts/answer/54048?hl=en
Just remove web history and uncheck private results.

This is a very good argument against government intervention. A government made of lawyers is not a good substitute for scientists and economists.

This seems to be the most accurate post on the topic, yet carries the lowest score. This is about making suggestions, not auto-sending messages.

Typing on a phone is annoying, so if I can say "on my way home" with fewer clicks, instead of having to retype the message all the time, I would be happy to do so. Of course, I could set up some kind of macro, but an automatic system is far easier for normal folks to use.

Of course, whether this should be patentable is an entirely different matter, but the feature is an entirely reasonable thing to try.

If you assume a linear traction-limited model[1], information only needs to be sent backwards. Specifically:
(a) current velocity
(b) current acceleration/deceleration.
(c) maximum deceleration.

The immediately following car just needs to measure the distance[2], and know its own state and parameters. Then it can calculate how much space it needs to stop if the car in front immediately starts breaking at the maximum rate. You can incorporate communication & reaction delays easily too, as well as any bounded noise on the state variables. This would work for an arbitrarily long chain of cars, with each car just needing to monitor the one in front of it. It even works for autonomous cars following human-driven cars[3].

I used to be a robotics researcher, with a focus on high performance navigation. The lane-free full 2D generalization of the problem above was a chapter in my thesis (from 2007).

[1] or just make sure your actual model fits within a conservative linear envelope.
[2] obviously you can estimate a&b, but it potentially introduces additional delay to get the noise down, in particular for acceleration since that is second order.
[3] this is an ideal case *IF* that driver is paying attention, since the human driver has a better perception system.

(I work at Google, but not on search)

When Google tries to do this, the same people start complaining about filter bubbles[1] and either turn off personalization in their search settings, or turn to DDG, where a primary selling point is that they don't personalize. You really can't have it both ways, although Google comes very close with a simple toggle button for personalized results[2].

I don't believe you appreciate the difficulty of search given the current state of advanced [black hat] SEO; things that worked in the past (such as plain pagerank) would not work at all today. All search engines must run to keep in place. Also, economics plays a role -- can those 10 things be implemented in a practical way that scales and is cost effective.

I can 10 things on my car that I'd like, such as better fuel economy, more horsepower, better crash safety, better visibility, more convenience features, and a lower price. Unfortunately many of those things conflict, so in a practical sense it is likely that the car company had to strike a balance. From my armchair I am unlikely to know all of the things that went into those trade-offs.
~~

[1] "filter bubbles" don't really apply to multi-answer ranking problems or are trivially broken with standard techniques from reinforcement learning to manage the "explore-vs-exploit" tradeoff. As far as I've been able to determine, the person who coined the bubble term has no formal background in statistics (in particular ranking problems) or machine learning (in particular reinforcement learning).

[2] An oberservation from a long-time logged-in user: In my search results, personalization hardly ever effects more than two results out of the first 10. In search at least, filter bubbles do not exist for me, and I've taken no steps to avoid them. They do happen when I listen to a music service for a while (where unlike search, only one song can be chosen as the next to play).

There already is such an option, called "verbatim":
https://support.google.com/websearch/answer/1734130?hl=en

... crash ... thunk ....
Boooooooooooooom!