6 ms·

All corporations grow, decay and rot on the similar trajectories, so in this sense it's similar. I worked at MS at this time, and I think BigG now is like MS i

by vl 8y ago

All corporations grow, decay and rot on the similar trajectories, so in this sense it's similar. I worked at MS at this time, and I think BigG now is like MS in 2003 roughly speaking. I'm waiting for miniggl to appear, then the circle will be complete. :)

mda 8y ago
Even with my limited insight I can tell that they were and still are vastly different companies.
- bg24 8y ago
  Agree. And the next Google which is born after, say 15 years, will be a vastly different company.
mav3rick 8y ago
Having worked at both. Google is far far ahead. Any young dev reading this, you will get good engineering foundations at Google that will keep you in good stead for the future.
- nostrademons 8y ago
  People said the same thing about Microsoft in the late 1990s - The Windows NT kernel was supposedly a thing of beauty, while Cairo was way ahead of its time. Problem is, computer science advances, so the type of programming that was state-of-the-art in the 1990s paled in comparison with what Google developed 10 years later. And the Google stack of 2003 is remarkably outdated by 2018 standards. As a Xoogler I use & value the engineering chops I learned at Google every day, but I'm not naive enough to think that'll last forever. There are some really exciting developments in multiple areas of computer science - notably blockchains, Rust, GPGPU, serverless - that Google is poorly positioned to take advantage of, as well as others (machine learning, search, big data, distributed systems, capability security) that Google has historically been the market leader at but that are rapidly being commoditized by very high quality open-source projects.
  - mav3rick 8y ago
    Right Google is losing in ML or not caught up to speed ? I don't know when you left but even outside Google it's pretty much known that ML innovation is Google's strength. Two big teams use Rust at Google in production. I guess Google didn't make the TPU or Tensor Flow as well. Take your pitch forks out but once you're done have a look at some facts.
    - nostrademons 8y ago
      Not losing, but commoditized - in those fields, there are now perfectly good open-source alternatives. I was at Google from 2009-2014. When I joined, Google was literally the only place you could work if you wanted to do data science on web-scale data sets. Nobody else had the infrastructure or the data. Now if you want to do Search, ElasticSearch has basically the same algorithms & data structures as Google's search server, with Dremel + some extra features thrown in. (The default ranking algorithm continues to suck, though.) If you want to do deep learning, you reach for Keras, and it'll use TensorFlow behind the scenes but with a much more fluent API. Hadoop was a major PITA to use when I joined Google; now in many ways it's easier & more robust than MapReduce, and the ecosystem has many more tools. Spark compares well with Flume. Zookeeper over Chubby. There are a number of NoSQL databases that operate on the same principle as BigTable, though I'd pick BigTable over them for robustness. Take your pick of HTML5 parsers (I even wrote and open-sourced one while I was at Google). Google was struggling mightily with headless WebKit for JS execution when I left, now you can stand up a Splash proxy in minutes or use one of the many SaaS versions. Protobufs, Bazel, gRPC, LevelDB have all been open-sourced, as have many other projects.
      - paganel 8y ago
        The big advantage of big companies like Google is that they have lots (and I mean lots) of data and for them that data is comparatively cheap to store and manipulate. I mean, I first wrote a text-categorization algorithm using a k-NN algorithm about 12-13 years ago, and in order to make it run with acceptable results I only needed to manually categorize about 200 articles for each category training set. That was very doable, both in terms of time spent for constructing the training set and in terms of storage costs. Now, I have been thinking for some time to write a ML algorithm that would automatically identify the forests from present-day satellite images or from some 1950s Soviet maps (which are very good on the details). I’m pretty sure that there already is some OS code that does that, but the training set requirements I think would “kill” the project for me. I read a couple of days ago (the article was shared here in HN) about some people at Stanford implementing a ML algorithm for identifying ships included in satellite images, and I remember reading that they used 1 million high-res images as a training set. Now, for me as a hobbyist or even for a small-ish company there’s no cheap way to store that training set. Never mind the costs of labeling those 1 million training images. Otherwise I totally agree with you, we live in a golden age of AI/ML code being made available for the general public, but unfortunately is the data that makes all the difference.
netheril96 8y ago
What/Who is miniggl?
- hawaiianbrah 8y ago
  A reference to minimsft, a blog from within microsoft about its culture

by vl 8y ago

mda 8y ago

Even with my limited insight I can tell that they were and still are vastly different companies.

bg24 8y ago
Agree. And the next Google which is born after, say 15 years, will be a vastly different company.

mav3rick 8y ago

Having worked at both. Google is far far ahead. Any young dev reading this, you will get good engineering foundations at Google that will keep you in good stead for the future.

nostrademons 8y ago
People said the same thing about Microsoft in the late 1990s - The Windows NT kernel was supposedly a thing of beauty, while Cairo was way ahead of its time. Problem is, computer science advances, so the type of programming that was state-of-the-art in the 1990s paled in comparison with what Google developed 10 years later. And the Google stack of 2003 is remarkably outdated by 2018 standards. As a Xoogler I use & value the engineering chops I learned at Google every day, but I'm not naive enough to think that'll last forever. There are some really exciting developments in multiple areas of computer science - notably blockchains, Rust, GPGPU, serverless - that Google is poorly positioned to take advantage of, as well as others (machine learning, search, big data, distributed systems, capability security) that Google has historically been the market leader at but that are rapidly being commoditized by very high quality open-source projects.
- mav3rick 8y ago
  Right Google is losing in ML or not caught up to speed ? I don't know when you left but even outside Google it's pretty much known that ML innovation is Google's strength. Two big teams use Rust at Google in production. I guess Google didn't make the TPU or Tensor Flow as well. Take your pitch forks out but once you're done have a look at some facts.
  - nostrademons 8y ago
    Not losing, but commoditized - in those fields, there are now perfectly good open-source alternatives. I was at Google from 2009-2014. When I joined, Google was literally the only place you could work if you wanted to do data science on web-scale data sets. Nobody else had the infrastructure or the data. Now if you want to do Search, ElasticSearch has basically the same algorithms & data structures as Google's search server, with Dremel + some extra features thrown in. (The default ranking algorithm continues to suck, though.) If you want to do deep learning, you reach for Keras, and it'll use TensorFlow behind the scenes but with a much more fluent API. Hadoop was a major PITA to use when I joined Google; now in many ways it's easier & more robust than MapReduce, and the ecosystem has many more tools. Spark compares well with Flume. Zookeeper over Chubby. There are a number of NoSQL databases that operate on the same principle as BigTable, though I'd pick BigTable over them for robustness. Take your pick of HTML5 parsers (I even wrote and open-sourced one while I was at Google). Google was struggling mightily with headless WebKit for JS execution when I left, now you can stand up a Splash proxy in minutes or use one of the many SaaS versions. Protobufs, Bazel, gRPC, LevelDB have all been open-sourced, as have many other projects.
    - paganel 8y ago
      The big advantage of big companies like Google is that they have lots (and I mean lots) of data and for them that data is comparatively cheap to store and manipulate. I mean, I first wrote a text-categorization algorithm using a k-NN algorithm about 12-13 years ago, and in order to make it run with acceptable results I only needed to manually categorize about 200 articles for each category training set. That was very doable, both in terms of time spent for constructing the training set and in terms of storage costs. Now, I have been thinking for some time to write a ML algorithm that would automatically identify the forests from present-day satellite images or from some 1950s Soviet maps (which are very good on the details). I’m pretty sure that there already is some OS code that does that, but the training set requirements I think would “kill” the project for me. I read a couple of days ago (the article was shared here in HN) about some people at Stanford implementing a ML algorithm for identifying ships included in satellite images, and I remember reading that they used 1 million high-res images as a training set. Now, for me as a hobbyist or even for a small-ish company there’s no cheap way to store that training set. Never mind the costs of labeling those 1 million training images. Otherwise I totally agree with you, we live in a golden age of AI/ML code being made available for the general public, but unfortunately is the data that makes all the difference.

netheril96 8y ago

What/Who is miniggl?

hawaiianbrah 8y ago
A reference to minimsft, a blog from within microsoft about its culture