Some MacBook Pro, MacBook Air and Mac Mini Models Will Become Obsolete Next Month, Lose Apple Repair Support

An anonymous reader writes: Apple will add certain MacBook Pro, MacBook Air, and Mac mini models to its list of vintage and obsolete products starting next month, which means the products will lose official Apple repair support through the company’s retail stores and authorized resellers. Kicking in on December 31, 2016, the MacBook Pro (15-inch, Early 2011) and MacBook Pro (17-inch, Early 2011) will become vintage and obsolete in all markets where applicable, while the Mac mini (Early 2009) and MacBook (13-inch, Mid 2009) will become obsolete worldwide on the same date.

Read more of this story at Slashdot.


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Lambdoku – AWS Lambda with Heroku-like Experience

README.md

Heroku-like experience with AWS Lambdas.

Features

Connecting current directory with lambda (like heroku git:remote)

$ lambdoku init

this allows you to omit the -a param for all commands below

Simplified environment variables management (heroku config)

$ lambdoku config:set ONE=1 TWO=2

$ lambdoku config
ONE=’1′
TWO=’2′

$ lambdoku config:get ONE
ONE=’1′

Simplified releases management (heroku releases)

$ lambdoku releases
22 | Setting env variables AA | 2016-11-26T21:12:46.894+0000
21 | Unsetting env variables XY | 2016-11-26T21:10:04.302+0000
20 | Setting env variables BB,XY | 2016-11-26T20:57:57.340+0000

$ lambdoku rollback 18

$ lambdoku releases
23 | Rolling back to version 18 | 2016-11-26T21:35:45.952+0000
22 | Setting env variables AA | 2016-11-26T21:12:46.894+0000
21 | Unsetting env variables XY | 2016-11-26T21:10:04.302+0000
20 | Setting env variables BB,XY | 2016-11-26T20:57:57.340+0000

in the example both code and configuration is rolled back from version 18.

Pipelines (heroku pipelines)

(actually the main reason why lambdoku was created)

$ lambdoku init lambdaDev

$ lambdoku pipeline:add lambdaStage

$ lambdoku pipeline:add lambdaProd -a lambdaStage

$ lambdoku pipeline
lambdaStage

$ lambdoku pipeline:promote

now lambdaDev and lambdaStage have the same codebase.
lambdaStage can


Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/QN3i2E3mncc/lambdoku

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Linux Kernel 4.4.35 LTS Hits the Streets with x86 Improvements, Updated Drivers

 softpedia: Hitting the streets only five days after the Linux 4.4.34 patch, Linux kernel 4.4.35 LTS is a small update that changes a total of 34 files


Original URL: http://feedproxy.google.com/~r/linuxtoday/linux/~3/tmMH83XtOQw/linux-kernel-4.4.35-lts-hits-the-streets-with-x86-improvements-updated-drivers-161127163508.html

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Configuring a High Interactivity SSH HoneyPot

Safety Advice

Remember running a Honey Pot is all about letting the bad guys in, therefore you’ll want to take steps to ensure the Honey Pot has no way of accessing your other systems. Remember it is solely your responsibility to ensure you secure your data and systems properly, and the author of this guide cannot be held responsible for any data loss or compromises you receive as a result of running a Honey Pot. Also bear in mind the attacker is likely to try and access stuff in the outside world or could try to use your Honey Pot to host content with legal implications, ensure you suitably firewall the front door to your Honey Pot also to just allow SSH access.
Introduction

In a world of evolving and targeted cyber threats understanding your attacker’s intentions and tools has never been more crucial. By deliberately maintaining vulnerable systems, or Honey Pots, and


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Dockerizing MySQL at Uber Engineering

Uber Engineering’s Schemaless storage system powers some of the biggest services at Uber, such as Mezzanine. Schemaless is a scalable and highly available datastore on top of MySQL¹ clusters. Managing these clusters was fairly easy when we had 16 clusters. These days, we have more than 1,000 clusters containing more than 4,000 database servers, and that requires a different class of tooling.
Initially, all our clusters were managed by Puppet, a lot of ad hoc scripts, and manual operations that couldn’t scale at Uber’s pace. When we began looking for a better way to manage our increasing number of MySQL clusters, we had a few basic requirements:
Run multiple database processes on each host
Automate everything
Have a single entry point to manage and monitor all clusters across all data center
The solution we came up with is a design called Schemadock. We run MySQL in Docker containers, which are managed by goal states that define


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