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I thought I was the only one who had this problem - so annoying, and the frequent Ui glitches when it asks you to choose an option .

I work with an engineer who is visually challenged and uses emacs with dictation to code. He is a pretty phenomenal engineer.

Presto is actively switching to use Velox as the backend ( https://github.com/prestodb/presto/tree/master/presto-native... ) . It is also being used extensively internally, again the paper describes these and their usages have grown , not reduced.

This isnt really true, Meta if anything has doubled down on Velox.

This is being actively used at Meta in Production across several engines ; the paper makes explicit references to this.

The fact that it's open for even commercial applications is going to ensure this spreads like wildfire.

My argument is not that 'I found interview easy and hence not so bad'. My argument is that in Lyfts case engineering hasnt been the problem with why its business is failing.

> Then the question becomes what exactly are you paying top dollar to these engineers for?

Again Engineering is one vector in a company's success but not the only one. It might well make sense for Lyft to pay well for good engineering talent there. Maybe not paying well might have doom'd them much faster.

Your take on OSS is also a naive one . OSS helps in off boarding long term maintenance costs (if the project becomes popular enough) and staves off bit rot. It helps attract talent , creates industry standards etc. Engineering is feature multiplier and for some companies it does make sense to OSS.

Its disingenuous to claim that these engineers werent delivering business value. James Gosling worked on Java at Sun and Sun failed, would you say that Gosling isnt a good engineer ?

Engineering can only work in the bounds of the problems set to it. If the leadership doesnt want to diversify streams , get into delivery for example there is nothing engineering can do there. In the end you need both good business acumen to succeed and engineering is just a multiplier and facilitator there.

This seems to be a common trope at HN where the failure of a company must be because of their hiring practice. Your comment also implies that the current failure has been engineering. This couldnt be further from the truth, Lyft had some of the best and smartest engineers. Lyft paid as well as they did because of the risk these engineers took in moving on from Google/Meta/Twitter what have you. FWIW, I found Lyfts interviews to be the easiest of the companies I interviewed at. The companies current downturn stems from the combined blow of COVID and focusing only on rideshare at the expense of not diversifying their revenue streams and their hiring or engineering has had very little to do with it.

If you look at the waves in the water you can tell they are different.

> Contrary to popular perception, Infosys is a valued company in many respects including HR policies, growth, and frugal innovation.

I am not aware of anything innovative that has come out of IT body shops, Do you have any examples of something innovative that they have done ?

Apple | Cupertino, CA | Data Engineer |Full time | Onsite

Apple is a technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services.

Apple's is looking for both junior and experienced engineers to work on big data, machine learning and high-scale, low-latency distributed systems. As a part of this team you will use machine learning at very large scale to build intelligent systems that operate at scale.

Requirements: - Ability to code in any statically typed language, excellent understanding of Data Structures and Algorithms - Experience and interest in Distributed Computing.

Nice to have: - Hand on experience with Spark/Spark streaming/Kafka - Hands on experience with Hadoop or large scale distributed processing.

- Functional programming experience in Scala (using monoids/semigroups etc in large distributed systems) If interested send your resume to [email protected]

NOTE : As of this moment we are not looking for new college grads and applicants should ideally have more than 2 years experience.

Apple | Cupertino, CA | Data Engineer |Full time | Onsite

Apple is a technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services.

Apple's ■■■■■■ team is looking for both junior and experienced engineers to work on big data, machine learning and high-scale, low-latency distributed systems. As a part of this team you will use machine learning at very large scale to build ■■■■■■■■ systems.

Requirements: - Ability to code in any statically typed language, excellent understanding of Data Structures and Algorithms - Experience and interest in Distributed Computing.

Nice to have: - Hand on experience with Spark/Spark streaming/Kafka - Hands on experience with Hadoop or large scale distributed processing.

- Functional programming experience in Scala (using monoids/semigroups etc in large distributed systems)

If interested send your resume to [email protected]

NOTE : As of this moment we are not looking for new college grads and applicants should ideally have more than 2 years experience.

Apple | Cupertino, CA | Data Engineer |Full time | Onsite

Apple is a technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services.

Apple's ■■■■■■ team is looking for both junior and experienced engineers to work on big data, machine learning and high-scale, low-latency distributed systems. As a part of this team you will use machine learning at very large scale to build ■■■■■■■■ systems.

Requirements: - Ability to code in any statically typed language, excellent understanding of Data Structures and Algorithms - Experience and interest in Distributed Computing.

Nice to have: - Hand on experience with Spark/Spark streaming/Kafka - Hands on experience with Hadoop or large scale distributed processing.

- Functional programming experience in Scala (using monoids/semigroups etc in large distributed systems)

If interested send your resume to [email protected]

NOTE : As of this moment we are not looking for new college grads and applicants should ideally have more than 2 years experience.

Apple | Cupertino, CA | Data Engineer |Full time | Onsite

Apple is a technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services.

Apple's ■■■■■■ team is looking for both junior and experienced engineers to work on big data, machine learning and high-scale, low-latency distributed systems. As a part of this team you will use machine learning at very large scale to build ■■■■■■■■ systems.

Requirements: - Ability to code in any statically typed language, excellent understanding of Data Structures and Algorithms - Experience and interest in Distributed Computing.

Nice to have: - Hand on experience with Spark/Spark streaming/Kafka - Hands on experience with Hadoop or large scale distributed processing.

- Functional programming experience in Scala (using monoids/semigroups etc in large distributed systems)

If interested send your resume to [email protected]

NOTE : As of this moment we are not looking for new college grads.

> doesn’t end here. BigQuery has background processes that constantly look at all the stored data and check if it can be optimized even further. Perhaps initially data was loaded in small chunks, and without seeing all the data, some decisions were not globally optimal. Or perhaps some parameters of the system have changed, and there are new opportunities for storage restructuring. Or perhaps, Capacitor models got more trained and tuned, and it possible to enhance existing data. Whatever the case might be, when the system detects an opportunity to improve storage, it kickstarts data conversion tasks. These tasks do not compete with queries for resources, they run completely in parallel, and don’t degrade query performance. Once the new, optimized storage is complete, it atomically replaces old storage data — without interfering with running queries. Old data will be garbage-collected later.

I wonder if they could share more details on how this is handled.

This assumes that the H1B's filed by the outsourcing companies are engineers your startup would want to hire. Unfortunately thats not true, most good quality engineers are almost never employed by these outsourcing firms. The general nature of their work also is mostly maintenance based , and it tends to attract bottom of the barrel 'engineers' who often couldn't find any other job. I am skeptical whether any of these 'engineers' would pass the interview gauntlet at many startups.

> Apple Watch is just ~4% after a year The Apple Watch just went on sale this year.

I am pretty sure nothing is streamed off of AWS - possibly Cloudfrount , but based on what I know of Netflix use L3 and Akamai heavily.

The Wii was sold at a profit. Also I believe they are still making a loss, since the component breakdown doesn't include costs of marketing/development etc.

The Standard and StandardPlus packages have the same configuration but the Plus package costs 15$ more. Am I missing something ?

They never seem to define what a Living room machine is ? I wouldn't consider a desktop be a living room machine.

I was rather hoping the keyboard would be included with the package. I think Microsoft can ill afford to charge so much for an accessory that is pretty much a USP for their tablets.

Really excited for the BufferBox and Plivio guys !

There is an episode of QI : http://www.youtube.com/watch?v=XCAg5_vHFmI where in they discuss about this , and also a brief interview with the mathematician involved.

Netflix doesnt stream off of Amazon and even if they do use cloudfront (which i highly doubt), they have other CDN's (Akamai,Limelight) off which they would stream traffic too. I do not see how it would be possible for Amazon to monitor that network activity and get information about Netflix.