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Male hypersexuality
Hypersexuality is a term used to describe excessive or compulsive sexual behavior. It can affect people of any gender and can be associated with a range of mental health conditions, such as bipolar disorder, borderline personality disorder, and certain types of addiction. It can also be a symptom of certain neurological conditions, such as frontotemporal dementia.
Hypersexuality can be problematic for a number of reasons, including causing relationship problems, financial difficulties, and social isolation. It is important for individuals who are experiencing hypersexuality to seek help from a mental health professional, who can provide treatment and support. Treatment may include therapy, medication, or a combination of both, depending on the individual's specific needs.
What is male hypersexuality and treatments
Male hypersexuality is a term used to describe excessive or compulsive sexual behavior in men. It can manifest in a number of different ways, such as excessive pornography use, compulsive sexual activity, or an obsession with sexual thoughts or fantasies. Like hypersexuality in general, male hypersexuality can be associated with a range of mental health conditions, such as bipolar disorder, borderline personality disorder, and certain types of addiction. It can also be a symptom of certain neurological conditions, such as frontotemporal dementia.
Treatment for male hypersexuality may include therapy, medication, or a combination of both, depending on the individual's specific needs. Therapy may involve working with a mental health professional to address the underlying causes of the behavior, such as trauma, stress, or relationship issues. Medication may be used to help manage the symptoms of a mental health condition or to reduce the frequency or intensity of sexual thoughts and behaviors. It is important for individuals experiencing male hypersexuality to seek help from a mental health professional, who can provide treatment and support. You can reduce your sexual problems by try this. click here

In my template (attached below), for some reason, the TOC prints and saves as blurry/pixelated text. However, when saving as PDF, the bookmarks still work.
The List of figures and tables are still (correctly) appearing as vectorised text.
Not sure what's going on and would appreciate any help/advice/suggestions. Thanks.
Link to my Word template

If you're looking for opportunities to learn and enhance your programming skills, consider checking out the various Java classes in Nagpur. As one of the most widely used programming languages, Java has a vast community of developers and enthusiasts. In Nagpur, you can find several institutes and training centers that offer comprehensive courses in Java programming. These classes are designed to cater to both beginners and advanced learners, and they cover various aspects of Java development, such as object-oriented programming, web application development, and more. So, if you're interested in building a career in software development, be sure to explore the Java classes available in Nagpur.

So at work we often hotswap our production and staging machines when upgrading. This means our staging machine should seamlessly take over for our production machine, meaning it inherits its IP-address 4 and 6, ssh hostkeys and hostname.
For context assume we have a master machine A which has access to servers B and C, but B and C can not talk with each other.
This is a little cumbersome so I thought about writing a python script which performs the swap. My code (which does not work properly) is to do the following
On host A targeting B

ssh -o StrictHostKeyChecking=no -t login-test01.com 'hostnamectl set-hostname login-test02.com' rsync -a -e 'ssh -o StrictHostKeyChecking=no' keys/login-test01.com/ login-test02.com:/etc/ssh ssh -o StrictHostKeyChecking=no -t login-test01.com 'nmcli conn modify ens192 ipv4.gateway "1.1.114.1" ipv6.method "ignore" ipv4.method "manual" ipv6.addresses "" ipv4.addresses "1.1.114.7/28" ipv6.gateway ""' 

On host A targeting C

ssh -o StrictHostKeyChecking=no -t login-test02.com 'hostnamectl set-hostname login-test01.com' rsync -a -e 'ssh -o StrictHostKeyChecking=no' keys/login-test02.com/ login-test01.com:/etc/ssh ssh -o StrictHostKeyChecking=no -t login-test02.com 'nmcli conn modify ens192 ipv4.gateway "1.1.114.1" ipv6.method "manual" ipv4.method "manual" ipv6.addresses "1:1:1:8044::7/64" ipv4.addresses "1.1.114.6/28" ipv6.gateway "1:1:1:8044::1"' 

Finally we restart network manager with a delay so that both machines swap IPs at the same time

ssh -o StrictHostKeyChecking=no -t login-test01.com '(sleep 10; systemctl restart NetworkManager.service) >/dev/null 2>/dev/null &' ssh -o StrictHostKeyChecking=no -t login-test02.com '(sleep 10; systemctl restart NetworkManager.service) >/dev/null 2>/dev/null &' 

Is there an issue with the sleep / restarting of the network manager? I can not really wrap my head around this.

Introduction

Data Science is a field of study that focuses on extracting knowledge from vast amounts of data using scientific techniques, procedures, and algorithms. It is used to identify hidden patterns in the raw data, which can be used to solve various business-related issues. Modern businesses today are required to deal with extensive data to improve their functioning. This is where data science deals with enormous amounts of data to provide solutions and help in effective decision-making to improve the business. As per the latest reports, data science is among the fastest-growing fields in the IT sector, and it is expected to grow by 27.9% by 2026. Data Science is a promising career path that ensures greater job satisfaction with excellent payments. Some standard job options with data science include Data Scientist, Data Analyst, Data Engineer, Data Architect, Business Intelligence Analyst, Statistician and Machine Learning Engineer. Various institutes offer courses in data science to help students build their careers. Among them, Croma Campus tops the list. Along with quality training, there are excellent Croma Campus Placement options that make this institution one of the best options to go for.
Read this section to learn about some of the best career options with data science and their required skills.

Career Options With Data Science

Some of the common job options after pursuing Data Science are as follows:

1. Data Scientist

A Data Scientist is a highly skilled professional who uses analytical skills to provide solutions for businesses and help in effective decision-making to improve productivity. Data Scientists are valuable to an organization and are responsible for gathering, processing, and interpreting data to facilitate effective decision-making. They must examine an enterprise’s sectors to detect problems and provide prospects for expansion. In addition, the Data Scientist cleans the data, eliminates unwanted data, and tests them to ensure the data are correct and consistent. The average salary of a Data Scientist ranges around Rs. 4 lakhs to Rs. 25 lakhs per annum in India.

1. Data Analyst

Data Analyst s hols a reputable position in IT companies. They are highly skilled professionals who examine and interpret data extracted from the data analysis to provide effective business solutions. They play a significant role in a business’s decision-making process and are responsible for adopting effective techniques to complete various projects successfully. The key responsibilities of a Data Analyst include data collection, preparation, and analysis, searching for patterns in multiple data sets, and data visualization. The average salary of a Data Analyst ranges between Rs. 1.8 lakhs to Rs. 12 lakhs per annum in India.

1. Data Engineer

A Data Engineer extracts and processes the information that Data Scientists and Data Analysts use later. These professionals ensure that analysing and interpreting information is effective, safe, and useful. The key responsibilities of a Data Engineer include data acquisition, identifying ways to improve the data quality, reliability, and efficiency, and preparing the data for predictive and prescriptive models. In addition, the Data Engineers use large data sets to address business problems and use sophisticated analytics, ML, and statistical methods to process the data. The average salary of a Data Engineer ranges between Rs. 3 lakhs to Rs. 22.5 lakhs per annum in India.

1. Data Architect

The Data Architects are responsible for building and designing massive data processing platforms to transform the data into information that supports various business-related decision-making processes. They design, create, supervise and administer the management of data structures. The Data Architects organize the information and data at all levels. They design both relational and non-relational databases that support various business applications. In addition, the Data Architects maintain data governance to ensure greater performance, scalability, and security. The average salary of a Data Architect ranges between Rs. 10.2 lakhs to Rs. 37.5 lakhs per annum in India.

1. Business Intelligence Analyst

Business Intelligence Analysts provide reliable information to businesses from various data sources and determine the critical priorities of a business. These professionals design and implement dashboards and business reports. They maintain Business Intelligence applications and provide advisory services on improving and modernizing analytics processes. The average salary of a Business Intelligence Architect ranges between Rs. 3.5 lakhs to Rs. 16.5 lakhs per annum in India.

1. Statistician

The Statisticians apply statistical methods to solve various issues and discover valuable information from the data. They are responsible for gathering, analysing, and interpreting the data, making predictions based on those data sets, and assisting organizations in making the right business decisions. The average salary of a Statistician ranges between Rs. 1.4 lakhs to Rs. 20 lakhs per annum in India.

1. Machine Learning Engineer

The Machine Learning Engineers understand the business objectives and develop ML models to achieve those goals. They research and implement effective ML algorithms and tools to achieve the best results. The Machine Learning Engineers verify data quality and are responsible for cleaning those data for processing. In addition, they train models and tune their hyperparameters, deploy the models to production and design various Machine Learning systems. The average salary of a Machine Learning Engineer is around Rs. 3.2 lakhs to Rs. 22 lakhs per annum in India.

Conclusion

To summarise, Data Science is a promising career path with several job opportunities like Data Scientist, Data Analyst, Data Engineer, Data Architect, Business Intelligence Analyst, Statistician and Machine Learning Engineer. Modern businesses extensively use this field of study for effective business-related decisions. Aspiring IT professionals can join Croma Campus to get the best training in Data Science and develop their skills significantly. In addition, the Croma Campus Student Placement cell is quite active and ensures dedicated placements with 80% course completion. Thus, such formal training guarantees a secure future in the IT industry and validates their skills as IT professionals.

Yesterday was our closest pick so far. Jalen Carter was the pick for the Lions. After 200 votes he had about 50% of the vote. However, over the next 648 votes, his share dropped down to just 39.5%. Interestingly, Christian Gonzalez (32.2%) and Devon Witherspoon (13.8%) were the two next highest vote getters and their total votes at the CB position exceeded Carter's share.
This is a good time to remind everyone you have the ability to edit your vote if you so choose. So for example, if you vote for Witherspoon and see he is a distant third but you had Gonzalez over Carter as well, you can go back and change it after seeing the results.
Up next today is the Raiders. This is an interesting spot. They just signed Jimmy Garapollo to 72.75/3 but only a year and a half of that money is guaranteed. Will they invest a high pick in Levis and make Jimmy an expensive bridge QB? Or do they think Jimmy has untapped potential with Davante Adams and Josh McDaniels? If that is the case they could look to upgrade their OL or invest in their secondary
Vote
Picks 1-6 and prior year results

TL;DR: Anyone out here using a driving route tracker that allows for checkpoints along a trip? Almost like a lap timer for a racetrack, but for a commute instead.
Here's what I've been doing so far:
I've made a Google Form that is very simple. It captures the date submitted, start time, and then asks for the time of each of the checkpoints I have made up along my route. I also have a time offset for if I take a detour to a coffee shop. I just input how many minutes and seconds I was off route.
I then made a Google Sheet that calculates the amount of time between checkpoints, splits month, day, and year, and then displays everything in a "Dashboard" of sorts. Example Here (Google Drive)
What you don't see in this is that I select the Month and Year so that I can see what my averages were for just one month, but I also have an all time. I'd like to be able to compare months assuming I have data year to year.
However, this is tedious work and I don't always feel like manually capturing the data. Also, there's a few ways my Form / Sheet doesn't really do what I'd like. For example my checkpoints are based on easy to remember locations along the route, not by distance. Due to this my graphs / charts don't really change much.
I'd like to find something that tracks travel times without me needing to input times during my travel. Is there a GPS app that lets you setup checkpoints and captures times / duration of travel?

My tradelink: https://steamcommunity.com/tradeoffenew/?partner=102474654&token=BH1q6eSo
My steam profile: https://steamcommunity.com/id/darknezz_/
Everything up for a trade, some skins might not be tradable yet
Buyouts are in cash (EURO), send trade offers
★ Butterfly Knife Rust Coat WW - B/O 630.10
★ Stiletto Knife Ultraviolet MW - B/O 269.54
★ Huntsman Knife Case Hardened MW -- B/O 161.07
★ Bowie Knife Case Hardened BS -- B/O 121.11
★ Gut Knife Autotronic BS -- B/O 114.23
★ Falchion Knife Scorched FT-- B/O 78.73
★ Shadow Daggers Night FT-- B/O 64.57
★ Navaja Knife Ultraviolet BS -- B/O 63.25
★ Navaja Knife Urban Masked FT-- B/O 61.90
★ Navaja Knife Forest DDPAT FT -- B/O 59.68
★ Moto Gloves Polygon WW -- B/O 147.35
★ Bloodhound Gloves Charred FT -- B/O 85.92
★ Broken Fang Gloves Needle Point MW-- B/O 67.07
★ Hydra Gloves Case Hardened WW -- B/O 59.88
★ Hand Wraps Constrictor WW -- B/O 57.32
★ Broken Fang Gloves Unhinged FT -- B/O 56.51
★ Moto Gloves 3rd Commando Company BS -- B/O 52.48
M4A1-S Printstream MW-- B/O 178.97
AK-47 Case Hardened FT -- B/O 157.29
AWP Graphite FN-- B/O 152.67
StatTrak™ AK-47 Neon Rider MW-- B/O 108.97
USP-S Printstream MW-- B/O 91.48
AK-47 Bloodsport FN-- B/O 80.07
AK-47 Bloodsport FT-- B/O 59.61
M4A1-S Golden Coil MW-- B/O 55.17
Desert Eagle Printstream FT-- B/O 43.74
M4A1-S Player Two MW-- B/O 38.59
USP-S Kill Confirmed FT- - B/O 38.28
M4A4 In Living Color FN- - B/O 37.66
AK-47 The Empress MW-- B/O 34.98
AWP Neo-Noir FN-- B/O 33.57
M4A1-S Chantico's Fire MW-- B/O 31.36
Desert Eagle Golden Koi FN-- B/O 29.86
USP-S Royal Blue FN -- B/O 28.86
AK-47 Asiimov FT -- B/O 25.49
And more…
Some prices might be outdated
My tradelink: https://steamcommunity.com/tradeoffenew/?partner=102474654&token=BH1q6eSo

My tradelink: https://steamcommunity.com/tradeoffenew/?partner=102474654&token=BH1q6eSo
My steam profile: https://steamcommunity.com/id/darknezz_/
Everything up for a trade, some skins might not be tradable yet
Buyouts are in cash (EURO), send trade offers
★ Butterfly Knife Rust Coat WW - B/O 630.10
★ Stiletto Knife Ultraviolet MW - B/O 269.54
★ Huntsman Knife Case Hardened MW -- B/O 161.07
★ Bowie Knife Case Hardened BS -- B/O 121.11
★ Gut Knife Autotronic BS -- B/O 114.23
★ Falchion Knife Scorched FT-- B/O 78.73
★ Shadow Daggers Night FT-- B/O 64.57
★ Navaja Knife Ultraviolet BS -- B/O 63.25
★ Navaja Knife Urban Masked FT-- B/O 61.90
★ Navaja Knife Forest DDPAT FT -- B/O 59.68
★ Moto Gloves Polygon WW -- B/O 147.35
★ Bloodhound Gloves Charred FT -- B/O 85.92
★ Broken Fang Gloves Needle Point MW-- B/O 67.07
★ Hydra Gloves Case Hardened WW -- B/O 59.88
★ Hand Wraps Constrictor WW -- B/O 57.32
★ Broken Fang Gloves Unhinged FT -- B/O 56.51
★ Moto Gloves 3rd Commando Company BS -- B/O 52.48
M4A1-S Printstream MW-- B/O 178.97
AK-47 Case Hardened FT -- B/O 157.29
AWP Graphite FN-- B/O 152.67
StatTrak™ AK-47 Neon Rider MW-- B/O 108.97
USP-S Printstream MW-- B/O 91.48
AK-47 Bloodsport FN-- B/O 80.07
AK-47 Bloodsport FT-- B/O 59.61
M4A1-S Golden Coil MW-- B/O 55.17
Desert Eagle Printstream FT-- B/O 43.74
M4A1-S Player Two MW-- B/O 38.59
USP-S Kill Confirmed FT- - B/O 38.28
M4A4 In Living Color FN- - B/O 37.66
AK-47 The Empress MW-- B/O 34.98
AWP Neo-Noir FN-- B/O 33.57
M4A1-S Chantico's Fire MW-- B/O 31.36
Desert Eagle Golden Koi FN-- B/O 29.86
USP-S Royal Blue FN -- B/O 28.86
AK-47 Asiimov FT -- B/O 25.49
And more…
Some prices might be outdated
My tradelink: https://steamcommunity.com/tradeoffenew/?partner=102474654&token=BH1q6eSo

I am a student, studying Cloud Computing and as part of my course, I am required to research Cloud Computing Environmental impact, Cloud computing's importance in modern life, Data center emissions, and ARM-based CPUs vs x86-based CPUs. I have attached a link to a Google form with some questions, no personal data is collected all of your answers are anonymous, if you could fill this in it would really help me out. All Answers are opinion based (No wrong or right) answers.
Thanks
Form Link

credit: https://dormando.livejournal.com/484577.html

The Technical Element

Design for change

- The old google mantra is right. Design for change. Change is having to deploy new software, upgrade existing software, scaling, equipment breaking, and people shifting around. - Everything in this mantra is about finding balance. You might think it's a good idea to tightly marry your system to a particular OS or Linux distro. It's just as bad of an idea to separately them entirely. Use layers and a _little_ indirection if you must. - This does not mean complete and total platform agnostics. It's about making one system two, two systems twenty. Dealing if a sysadmin gets hit by a bus, if that dangling harddrive dies, if someone runs rm -rf /. It's for the incremental changes. Security updates, pushing new corporate content.

Use automatic, repeatable builds

- Don't build anything by hand. If you do, do it twice, and grab every single command the second time around. - I cannot stress how important this is. It should take no more than 15 minutes from bare metal to production for new hardware. There does not need to be a human element to screw it up, or get punished when a server goes down and no one knows how to replace it. - This is true for anything. There is _no_ such thing as a "one off" server build. If you've built it once, and it only needs to exist once, it will exist twice. The second time around is when it breaks, or if you need to do a major upgrade or consolidation two years down the road and have no frickin' clue how it was put together. - Test, vet new builds. This should be easy because your builds are all automatic, correct? - Scripted builds means that upgrade from Linux Distro Version 3 to Version 4 is absolutely clear cut. Install Version 4 and test the scripts. Read documentation and fix until it works again. This should be a week's worth of work at most, not a yearlong project. (to finish just in time for Version 5 to come out!).

Use redundancy

- Just beacuse something might be easy to rebuild, doesn't mean you can ignore redundancy. Jump boxes, mail servers, billing gateways, whatever. Wouldn't it be a hell of a lot easier if you could swap out one half of the equation without causing downtime for your customers? - ... and along those lines, you get to "deal with it later!" when a box goes down at 3am, and the redundant machine kicks in. - Even if it's not ideal, go for it anyway. Rsync'ing configs to a second box is a step above nothing. DRBD might not be perfect but it can provide an amazing service.

Use backups

- We shouldn't even joke about this. Use harddrives, burn the tapes. Compress them, move them, run them in parallel. Backup EVERYTHING! - If your builds are automatic, the entire process can be backed up. If you're following along past this point a *real* Disaster Recovery plan might not seem so far fetched.

Keep monitoring specific

- Monitor every damn thing you can, but do it right. Don't get a thousand alerts if your NFS server craps its pants. Don't alert on timeouts if it doesn't make sense for your system. Test for success at the most specific level; sure the service might allow a new TCP connection, and it might even say hello, but does it remember how to do its job? - If you have 500 webservers, you probably don't need to know immediately if one goes down. You _should_ know if the load balancer decided to not take it out of rotation and real human people are seeing its uglyriffic error messages.

Graph data, keep exact historical data

- Graphs are for visualizing trends. Historical data is for crunching numbers. Don't mix the two! It's too easy to get wrong numbers from eyeballing graphs. Many sites use rrd's or other aggregating data systems which will average and smooth out data over time to save on storage space. This means it's not only hard to read, it's wrong. - Don't get trapped having to skim through hundreds of graphs just to pinpoint an issue. If you're trying to find outliers in the graphs, you can pull those out via scripts as well. - If you must use graphs for troubleshooting, try to aggregate high level concepts into a single page, which link into drill-down pages from there. If you can see a spike in the database load, you'll know to click to the page overviewing the databases, then you'd see the one or two iffy machines in question. The idea is to narrow something down fast. Remove as much guesswork as possible.

Log useful information, use multiple streams of data

- Work on your own, and with development, to log as much useful information as you can. Doesn't matter if you live analyze it and store the data somewhere, or lump it into a database and run reports. Information is useful. - Useful examples: Page rendering time (what page, what box, etc), user-facing errors, database and internal service errors, bandwidth usage, etc. - Establish graphs, reports, and do historical comparisons from generalized data. - Reports are really important. Get digested data week-to-week or day-to-day about changes in your infrastructure.

Understand your data storage, databases

- There's an entirely separate set of undrestanding about operating databases, but sometimes you can't leave all of this up to your DBA. - Having multiple, redundant databases affords you many luxuries. Operations that were once many hours of downtime can be done "online" without shelling out for a huge Oracle instance. MySQL and replication is a fantastic thing. - Work with the DBAs to get the best possible hardware for the database in question. RAID10, gobs of RAM, many fast spindles, and potentially RAM disks and SSD's. Ops has access to the vendors, DBA's can beat the pants off the hardware. Find out what works best and save tons of cash in the long run. - Database configurations are changing. Software like HiveDB, MySQL Proxy, DPM exist now. We're absolutely doing partitioned data for huge datasets. We're also thinking outside of the box with software like starling and Gearman. Learn what these are, and understand that not everything will be in a database. - Get a good grip on your filers! If the data's important, back it up! Snapshots on monolithic NFS servers are fantastic, wonderful, and NOT a backup! - Consider alternatives. MogileFS gets better year after year. There're likely other projects for freely and cheaply maintaining massive stores of files. Similar systems were developed for youtube.com, archive.org, etc. We're finally free of expensive NFS filers being the standard!

Scale out a lot, up a little

- You've seen all of the papers. Scale out is really the way to go. Get commodity (read: available, affordable, standard, NOT super cheap) hardware and work with everyone to ensure all aspects possible can scale out. - Scaling out starts at two, work from there. This also happens to encompass redundancy. - Scale out as far as you can without being idiotic about it. The example of MySQL replication with single master, many slaves, is a fantastic example of one form of scale-out sucking. All slaves must do all writes, so as the number of writes scale up with the reads (if they do for your app, which I bet they certainly do), you get less capacity per slave you add. - Keep alternatives in mind. User or range partitioning onto many databases, avoiding production slaves where possible, etc. Good ideas, many ways of implement. - Everything can scale if you give it a chance! Routers, switches, load balancers, webservers, databases. - Remember scale up? Big evil machines with many slow cores, lots of IO boards, and very expensive storage equipment? They're coming back. Well, the CPU part is. - RAM is cheap. - Combine the two, and you just may end up combining services again. A load balancer here, a webserver there... If an application can use many CPUs (apache) this is perfect. If it can't (memcached doesn't get much benefit from it, usually) you can end up wasting tons of available resources by segregating services too much. - Job systems could potentially fill in gaps here. Where there're extra cores, slap up more workers.

Cache

- Caching is good. Developers, sysops, etc. Get on this! Yes, it's weird. It's different. Sometimes you may even need to, gasp, make a tradeoff for it. Effective use of caching can have as much as a ten times increase in overall system performance. That's a giant magnifying glass over the systems you have already and a fraction of the overall cost. - Memcached. Service cache, denormalize DB structures (where it makes performance sense!), squid cache, or even make better usage of OS caches. - Test it, toy with it, and break it. There will be new and different problems with caching. Be prepared for it.

Asyncronous jobs

- Starling, Gearman, The Schwartz, whatever. Job systems allow much more application flexibility. Workers can be spawned one-off, be persistent (load cached data, prepare data, etc), be on different hardware, different locations, and be syncronous or asyncronous. - Maintaining these things is an ops issue. Using them is both a developer and an ops issue. - User clicks "send all my friends an e-mail". Schedule a job, immediately say "okay done! Your friends will receive your spam shortly!" - let the job service multiplex and deal with the issue. - Job systems are great places to bridge services. Blog post -> IM notification, billing cron -> billing services, authentication gateways, etc. - Easy to scale. There will be choke points for where requests come in, and all the workers need to do is pull. This is in contrast with the largely push/pull state of HTTP.

Security and patrols

- Install security updates! Seriously! There's a whole crazy network of people who are dedicated to giving these to you in the shortest period of time possible. Don't let them sit for _years_ because you're afraid of change. - Security is in layers. Accept what you can and cannot secure. Just because mysql has password access doesn't mean it gets to be directly accessable by the internet. - Disable passwords over ssh. Use passphrase encrypted key auth. Remote users _cannot_ guess your private key. They _have_ to get it from you. Keep it safe, and there's no point in firewalling off your ssh port. - Understand how the application works, exactly what it needs to do, and work that to your advantage. If the only part of your application which needs outbound internet access _at all_ are the billing pages and some twitter-posting service, those can easily become job workers. Put the job workers on specific boxes and allow those access to specific hosts. Keep the rest of your network in the dark. - The above is especially important for php sites, but probably works great elsewhere. If someone breaks in, it's most likely going to be through your application. When someone gets in through the front gate, they'll need to haul in their toolbox to get into the safe. Don't let them pull in data and get what they need, or upload the contents of your database somewhere! - These specific suggestions aside, read a lot. Use your best judgement, and test. If you have no understanding of how a security model works, that might not immediately make it worthless, but you certainly don't know where its limits are or even if it works. - Secure based on testing, theory, attack trees, don't stab in the dark. I love it when people dream up obscure security models and ordinary folks like me can smush it to crumbles. - Patrol what you can! Audit logins, logouts, commands used. All accesses to external facing services, including all arguments given in the request. Find outliers, outright ban input outside of the scope of your application, and do what you can actively and have the data to work retroactively. - If you suspect something's been cracked, *take proper precaution* and understand a little computer forensics (or get a company that does). Respond by removing network access, checking the system through serial console or direct terminal, and avoiding using any service, config file, or data on the compromised machine. Too many people "clean up a trojan' and never understand how it got there, or if they've _actually cleaned it up_. - If you do have a security team, forensics expert, or anyone else onhand, you must touch the machine as little as possible and isolate it. This means not rebooting it to "clear out some funky running processes". They need to be able to get at those. If you need to half ass it, go ahead, but remember to wipe the system completely clean, apply any security updates, and do your best to figure out if they've compromised any important data. Do what you can. - Security is an incredible balancing act. If you do it wrong, developers, users, etc, will revolt and find ways around it. If they _can_ get around it, you're not doing your job right. If they _can't_ get around it, they might just give up and leave. - Keep an iron grip on access control. This means ops must absolutely provide windows for what doors have been locked. Kicking development off of production entirely means they get to stab in the dark on fixing hard problems. Providing logging, debugging tools, etc, without allowing them to directly change the service, will be a win for all aspects of production.

The Human Element

Learn from many sources

- Fill up some RSS feeds, and read at least a few good articles per week. LWN, kerneltrap, undeadly.org, whatever's relevant, or even loosely related, to what you do. - Read blogs from smart people. Sometimes they post interesting topics, and comment streams give us the unique ability to directly converse with the masters. - Read a few blogs from not so smart people. Get a feel for what stumps them, or what they do that doesn't work so well. - Get to know people who can kick your ass, at anything. Stay humble. - Help find your own strengths by taking in from many sources, and gobbling up what envigorates you. - Read up on success and failure stories from other companies. Ring up their CTO's and get them to divulge advise over free lunch.

Try many things

- You'll be amazed at what you can do if you keep trying. Never seen something before? Give it a shot. - Try to not be a dangerous newbie. Play in the sandbox until you're comfortable enough to not burn down the house.

Understand redundancy

- Really understand how redundancy affects things. How it works, how it doesn't work. - Break redundant systems in a test lab, sometimes in production. Learn what you can while you're in control. Unplug the power, yank cards out, kill processes, run the box out of memory, yank a harddrive, yank ethernet. - Test replacing and upgrading systems in a redundant setup. Maybe you can toss in that brand new hal-o-tron 8000 without taking downtime.

Understand scalability

- There're tons of papers on making scalable systems. Even if you can't write one yourself, try to understand the theory. - Learn with virtualization. Set up a few virtual machines and try tossing up applications to multiple machines. Run multiple instances locally on different ports. - It's usually the job of operations to do proper capacity planning. You won't know what to do add unless you truely understand where resources should be added.

Become a troubleshooting superstar

- The moment something breaks the clock is ticking. You must be able to pull out your arsenal and use them effectively. - Practice troubleshooting. Pick a perfectly good, working page, and try to track down how it works. - strace, ltrace, lsof, logs. - Understand that load != load. Look at all available information as to how a host is performing or behaving. - Be very familiar with the tools for your IO system. Often "mysterious" performance problems happen beacuse your RAID or SAN setup isn't happy for some reason. - Leave documentation. Checklists, troubleshooting tips, build tools. - Build more tools. For yourself, for other people, or add features to existing ones.

Work with IT

- Believe it or not, there is overlap. - Ops has to maintain high bandwidth network access for servers. IT has to do the same for people, and is often the bridge ops has *into* the datacenter. It may make sense to work together on this one. - Draw the right line. IT should manage mail, but ops should manage development servers. Don't offload things you don't need to, and offer to do what you do best if necessary. - Don't alienate people. Macs are popular, linux is (slowly) gaining share. Believe it or not, forcing everyone to use microsoft productivity software can bite you. There are plenty of alternatives, try one. Odds are more people in your company are familiar with google apps than they are with outlook. - Don't make it more difficult than you have to do for people to run a unix system natively. Unless your backend is a windows shop, wouldn't you want people to have more familiarity with the OS they're supposed to support?

Work with developers

- You both work on the same product, for the same purpose. Try working together a little more. - Having strategy meetings is not working together. - Development understands the code resources the best, and operations understands the hardware and deployment the best. You can design something more efficient by taking all of this into mind. - Cross training. Disseminating information can show how tools and designs on both sides can be improved to be more manageable and resilient. - Be careful of being too demanding on either side. It's not an Us vs Them. Everyone's human. Everyone should be doing as much as they can for the company, not for themselves. - It's more pleasent to handle crunch times and emergencies when everyone gets along.

Work with ops

- Ops folks have their specialties. Networking, databases, OS. Don't forget to talk to each other! - Getting stuck in a rut is demotivating, boring, and a good way to lose people. Even if your systems ops guy has the ability to look over the shoulder of the network guy, they have the opportunity to learn. - Always give people an opportunity to try, learn, and grow. - Be careful of rewarding your best with too much work. If there're people who can pick up slack, you use them. - Bad eggs. It happens. Be tough enough to deal with them. Most people can be turned around with a little help, but they need to be able to be independent.

The Practice

Fix it now, not later

- If a webserver goes offline, don't care about it. You have ten spare, right? - Pick a day during the week to sweep up broken crap. Replace any broken hardware, ensure everything's 100% before swinging into the weekend. - If small, annoying problems crop up, fix them permanently first thing in the morning. Logs fill up the disk twice last week? Come in fresh the next day, and fix it for good. These stack up, and suck. - If you have automated builds, use this to your advantage to fix what you can right away, or in bulk.

Automate everything

- Humans can't screw up scripted tasks (as easily). - Do it twice. Once by hand if you must, then roll up what you did into a script. - Commented scripts make fantastic documentation. Instead of writing twenty pages detailing how to install something (which is up to interpretation of the reader!), write a script which explains what it does. - Scripts can be rolled up into automated builds. The more often something is done, the closer it should get to becoming a zero time task.

Change what's necessary

- Make small, isolated changes. - If you don't have to change it, leave it. - This also means you must understand _when_ to change. Find what's necessary and upgrade it, switch it out, make it standard.

Design for change

- If you can't do it right immediately, get on the road to it being right. - This means if you don't have time to do something right, get the basics going with a clear migration roadmap to the right thing. While your new mail system might not be the crazy cool redundant bounce-processing spam monster you dream of, installing postfix and setting up two hosts with a clean configuration gets you closer than you might think. - This does have a tendency to leave unfinished projects everywhere, but you were going to do that anyway. :)

Practice updating content, fast

- It's usually the job of operations to push out code. Don't suck at it. Push in parallel, apply rolling restarts, be an efficient machine. - This includes software updates, security patches, and configuration changes. - Use puppet, cfengine, whatever you need to control the configuration. Keep it clean, simple, and easy. - The fewer files one must change to make a necessary adjustment the better. If you're adding one line to 20 files just to push out a new database, you're doing it wrong. Build simple templates, build outward, and don't repeat data which needs to be edited by hand.

Standardize, stick to the standard

- Pick one or two standard OS's, httpd's, databases, package systems. - Stick with them. Adjust and upgrade methods as it makes sense. - Don't stick with that major version forever. Unless your product is going to be feature frozen forever, you'll need to keep the standard rolling forward, and everthing behind it. - The _more_ is standard, the more places your tools will work. The more packages for other parts of the operation will "just work" everywhere else too.

Document well

- Document process - Document product - Categorize into shallow trees. - Don't redundantly document. If a script has a long help, ask the reader to refer to that. The closer the documentation is to the program being discussed, the more likely it is to stay accurate. - Marry documentation into code. perldoc, pydoc, etc. - Out of date documentation is poisonous. Reserve time to keep things up to date. Sit down with new employees and update documentation as they run into problems. - Use ticketing systems, with moderation. Documentation of history is important as well. Forcing people to create detailed process tickets for a DNS is just pissing in other people's cheerios.

Use source control

- Use git, or mercurial. Avoid SVN like the black plague. - Put all of your configurations, scripts hacks, whatever, into source control. - Keep checkouts everywhere... - Keep strict, clean, master checkouts. No one should be able to push changes that aren't comitted, but it should also be easy to test changes (in a VM, directly on a single test machine) without having to wrestle with the source control.

Hire well

- Discern between stubborn and smart - Don't avoid hiring senior. Some people really know their shit. Some _seem_ like they do. Others are "senior" in a particular area and will fall behind as technology changes. While you might want to avoid some, there are definitely rockstars out there. - Don't avoid hiring junior. I know so many people who've started really junior (including myself! I still view myself as junior), who've shot up through the ranks and are now have firm established careers. I'd believe most of us have. Except there are ones who don't learn, don't have the motivation, or are in the wrong field.

Avoid vendor lock in, and keep a good relationship with the vendors you do use

- Buying propreitary hardward has the major downside of potentially locking you into always using it. It might be a particular SAN, NAS, special-case direct attached storage, backup systems, etc. Avoid getting sucked in. If you follow all of the above design advise, one should be able to build test environments on different platforms quickly. You're then able to keep on top of hardware evaluations and keep choices open. - If everything's deep, dark, gnarled, undocumented, and directly dependent on your fancy proprietary load balancer, you'll never wriggle free of it. - Be nice to the vendors you do end up using. If you "push them _hard_ on price!" for every single purchase, expect some shit hardware to show up. - Datacenters these days have a lot of potentially useful resources. Try to throw some free remote hands service into your contract and abusing that to get harddrives replaced, vendor items shipped/RMA'ed, and some basic hardware installs. I've had entire racks of equipment delivered and installed with barely a visit from an employee... and damn, it's nice.

Give Open Source a serious try

- nginx, mongrel, lighttpd, apache, perlbal, mogilefs, memcached, squid, OpenBGPD, PF, IPTables, LVS, MySQL, Postgres, blah, blah, blah. Before you hop back on that trusty, reliable, expensive proprietary setup, give open source a shot. You might find yourself adding plugins, extensions, code fixes or contracting help to bring features you'd never be able to do otherwise. In my own experience OSS is just as reliable, often moreso, than big expensive hardware when put under significant load. - The idea of "you get what you pay for" is a complete lie. If you can't make OSS work for you and need the hand holding, you _can_ still go with a vendor. If you have a smart, motivated team, who really want to learn and understand how their infrastructure runs, you just can't beat some hardy GPL'ed or BSD'ed systems. - MySQL and Postgres are fine. Call them tradeoffs if you will; nothing's going to crawl out of your closet and night and eat your data. Sure, it does happen, but you're much more likely to be screwed over with monolithic oracle instances going offline (it happens!) than you are with a well tested and stable MySQL instance, in a redundant master<->master cluster pair. - I'd say 'cite references' - but go look around. Check out any number of articles on the LAMP stack. Most major dot coms, ISP's, and even corporations now are adopting. Give it a shot. The worst you'll have is some lost time, and another product to scare your vendor into dropping price with.

The Results Of The Lip-Syncs are in!
Raja ⭐ (1x win) , Condragulations, you’re a winner baby!
Trinity The Tuck, good job this week, you are safe!
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💚 Pixie Polite 💚, shantay you stay!
🟥 Princess Poppy 🟥 , sashay away... (12th place)
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EP2: 🐁 "Rats: The Rusical" 🐁
The queens are settling in, but the pressure is on as their singing, acting and dancing skills are truly challenged in 'Rats: The Rusical', Drag Race's first ever live musical. West End star Sheridan Smith joins Michelle Visage and Alan Carr on the judging panel as another queen exits the competition. (Rusical Challenge).
Rats: The Rusical

Queen	Role
Art Simone	Jane
Raja	Scat Rat
Jade	Scabies
Inti	Rat Pack #1
The Princess	Rat Pack #2
La Grande Dame	Rat Pack #3
Trinity The Tuck	Miss Dysentery
Adriana	Depravity
Danny Beard	Evita Von Fleas
Ariel Rec	Dame Doody Stench
Pixie Polite	Specimen One

Poll
Spreadsheet/Track Record

All of my languages I have yet created inhabit the same continent in my conworld, and for worldbuilding purposes, I kind of want the continent to have only been settled in 2, maybe 3 distinct migration events. So there's an artificially imposed necessity to force every ethnic group, and by extension their language, to trace back to one of those 2-3 original groups.
That means I have to find a way to smoosh my existing languages into a limited number of macrofamilies. It will bother me, and has been bothering me to the point of not being able to make any progress on any languages at all, unless I can find a way to link together formerly disparate families.
What has made it all the more infuriating is that a bunch of my languages have just enough suspicious cross-linguistic morphological similarities to suggest a connection to half the other languages, but not to the other half.
I've gathered together all the morphological data I can think of that I've been agonizing over in my head for the past... several months? to see if anyone else can spot any obvious similarities, or lack thereof, across 4 different languages from 4 different families. As it stands now, Argai (< West Celean family) and Eken Dingir (< Dingir family) are assumed to descend from a common ancestor, and the strength of the connections between that, Apshur (< Transtleic family), and Mtsqrveli (< Tskhri-Zani family), is unknown.
I don't know if this is enough data to make the call, but if anyone is willing to peruse the Google sheet and hazard a guess as to whether Apshur is probably closer connected to Mtsqrveli or Argai-Dingir, if either, I would greatly appreciate it.

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