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Worry never Rob’s tomorrow if its sorrow, but only saps today of it’s strength.

A.J. Cronin


Loneliness is the poverty of self; solitude is richness of self.

May Sarton


Why Some of Instagram’s Biggest Memers Are Locking Their Accounts – The Atlantic

A chain with a lock drapes over a sign that says "This Account is Private. Follow to see their photos and videos."

Over the past six months, some of Instagram’s biggest meme pages—like Shithead Steve, with more than 2.5 million followers, howitlook.s (8 million), couplesnote, (8.2 million) greatercomedy, (5.3 million), Pubity (5.1 million), and more—have locked down their accounts, forcing non-followers to request access in order to view their content.

“I’m getting real SICK of private meme pages,” one Twitter user posted on Monday. “There’s no logical reason for these insta meme pages to be on private,” another says. “Can we all start boycotting private meme pages on IG ???” begs another.

But while followers may hate it, going private is a new way for professional and semi-professional Instagrammers to stay afloat in a crowded market on an increasingly volatile platform.

Going private on Instagram means only people who follow you can see and share your content. If a friend drops a link to a funny meme from a private page into a group chat, only those who already follow the page will be able to see it. So, the thinking goes, anyone who wants to see it and can’t will smash the follow button.

“People go private because they get more followers when a follower sends a post to their friends and that person has to follow the account in order to see. It’s that simple,” says Jack Wagner, a Los Angeles–based director who has run several meme accounts. “It’s just a weird technique somebody noticed one day and now lots of people do it.”

Reid Hailey, the founder of Doing Things, a media company that manages a network of Instagram pages with a collective 14 million followers, says that around 75 percent of the accounts he oversees are set to private. “If you’re public, people just always see your stuff and they don’t feel the need to follow you,” he says. Hailey sees it as partial relief for stagnant follower counts: “It didn’t really become a mainstream thing until the algorithm started hitting hard I would say about six months ago or so. People are hurting for growth. A lot of meme pages aren’t really growing.”

Hailey says that before one of his large accounts went private, it was growing at an average of 10,000 new followers per week. Once it flipped to private, that number jumped to 100,000.

Reaching the Instagram Explore page, which surfaces posts from accounts you don’t yet follow, used to be the default way to grow for most memers. Just a couple years ago, large meme pages would jockey for spots on Explore, often forming Instagram Pods or groups aimed at gaming the algorithm. Now, going private is the faster way to grow.

Several months ago, Sonny, who runs several large meme pages and goes by only his first name online, flipped four of them to private. He says that the growth on those accounts has far outpaced anything he’s been able to get from making it on Instagram Explore.

“When you run a private account, you don’t get in the Explore page anymore, but I’ve noticed it doesn’t matter,” he said. His main account, @Sonny5ideUp, is still public, and gets around 200 followers if it reaches more than 1 million people via the Explore page, he said. But his private accounts can get as many as 10,000 new followers in a day.

Some top meme admins also think private accounts are less likely to be unfollowed. Unfollowing a public account just takes one click, but when a user goes to unfollow a private account, a pop-up appears asking them if they really want to take that step and reminding them that they’ll have to re-request if they want access again. Many people find it easier to just keep following.

Some Instagram meme pages flip-flop between public and private as a way to game both systems. Many will tease the possibility of going private, posting announcements in their page bios like, “Going private in the next 24 hours,” to entice people to follow while they can.

To some memers though, the hack feels desperate.

Elliot Tebele, the founder of the Fuck Jerry Instagram account, which currently has more than 13.7 million followers, says that while he’s dabbled in going private, overall he doesn’t think it’s the right strategy for his brand. “I’ve tested it once or twice to see and it does work,” he says. Tebele says that growth hacks like this tend to come and go (commenting on celebrity photos is another popular way to get your account noticed right now), but it’s more sustainable to hunker down and focus on building your public brand.

“It’s a frowned-upon practice in the meme-page community because it makes your account feel far less genuine,” says Gage, a memer in Missouri who runs the page @yung.crippling.depression. “I see that way of growing your page as slightly selfish.”

Jackson Weimer, a writer at Meme Insider who also runs the meme page @Hugeplateofketchup8 says that while he understands the criticism, there are other benefits to going private aside from just follower growth. He says that many memers who trade in risque or off-color humor believe that going private decreases the likelihood that their content will be reported. “Stuff is more likely to get banned when your posts are a little edgy and hit the Explore page and people who might be triggerable see it and report it,” he says. “Going private in the meme world is thought of as being safe in terms of keeping your account.”

Ultimately, going private is a reaction to Instagram’s opaque and ever-changing internal systems for surfacing posts. Admins of several large meme accounts say the Instagram meme market is more competitive than ever, and making it onto Explore is harder the larger your page gets because it takes a significant amount of engagement on one post to make it on there. Consequently, DMs and group chats have become memers’ new promised land.

“The final destination of all our content is your friend’s DM,” says Barak Shragai, the CEO and co-founder of IMGN Media, which works on several large Instagram meme publishers including Daquan.

“If people see your meme on Explore they might not even like you,” says Sonny, “but if it’s going into a curated DM thread where everyone is sharing relevant memes, it’s more relevant.” It’s for this reason he thinks people are more likely to engage with memes they discover via DM rather than Explore.

Still, though, frustrated users have found a work-around: “Meme accounts making their pages private on Instagram,” lamented one woman on Twitter, “As if I’m not going to screenshot it and send it to my friends anyways.”

via Why Some of Instagram’s Biggest Memers Are Locking Their Accounts – The Atlantic


Red flags in data science interviews – Towards Data Science

Red flags in data science interviews

This post was co-written with Emily Robinson, a data scientist at DataCamp. Check out the rest of her blog posts, including ones on applying to data science jobs, finding career sponsors, and giving your first data science talk.

When interviewing for any position, you should be evaluating the company just as much as they are evaluating you. While you can research the company beforehand on glassdoor and similar sites, interviews are the best place to get a deeper understanding of the company and ask important questions. Companies will never straight up tell you they are bad to work for, so you have to look for the signs yourself.

Here is our list of 12 signs the company you are interviewing with for a data scientist job should be avoided (and the questions to ask during the interview). The first six mainly apply to companies that already have multiple data scientists or analysts. If you’re thinking of joining a company as their first data scientist, you’ll face a whole different set of challenges, including most likely doing a lot of data engineering work (see flag 1) and spreading a data science mindset. Someone has to do it, but we generally advise against it for your first data science role unless you come from an engineering background and want to do that work. If there’s just a data science leader and they’re building out a team, ask how they plan to handle the issues raised below, but keep in mind it’s always easier to promise an ideal system than implement one.

Red flags on how the data science team runs

1. No data engineering or infrastructure.

Data science requires data to be easily available for analysis. If the company doesn’t have a well-maintained data infrastructure, you won’t have what you need to do your job. A data engineer is a person who prepares data for analysis, and if your company doesn’t have them you’ll have to do the work yourself. If you feel qualified to take on the role of a data engineer that may be okay, but otherwise you’ll be struggling to deliver anything of value.

Question to ask during the interview: what is your data infrastructure like and who maintains it? What format is the data typically in (Excel, a SQL database, csv)?

2. No peer review between data scientists.

A strong data science team will have ways to ensure mistakes don’t slip through the cracks. These can include code reviews, practice presentations, and consistent check-ins with the team. If the team doesn’t consistently do these, mistakes won’t be found until the work is already delivered, which usually ends with someone getting reprimanded.

Question to ask: what steps does the team take for QA and peer review?

3. No standard set of languages on the team.

Many data science teams take the approach of letting anyone on the team use any language they want. The idea is that if everyone uses their favorite languages work will be completed faster. There is a huge problem with this: when everyone uses separate languages, no one will be able to pass off work to anyone else. Every data science task will have a single person responsible for it, and if they quit, get sick, or just need help no one will be able to do so, creating a very stressful environment. It’s fine to use R, Python, or even dare we say SAS, but just have a consistent set of languages amongst the team.

Question to ask: what languages does your team use, and how do you decide whether to adopt a new one?

4. They don’t understand the data hierarchy of needs.

Similar to not having a data infrastructure, some companies get really excited about concepts like AI without having the foundation in place. Machine learning and AI require a company to have a high level of data science maturity, including understanding how to build models, their limitations, and how to deploy them. You might get blamed when their unrealistic expectations meet reality.

Question to ask: how does the company balance spending time on complex approaches like AI with foundational work like cleaning data, checking data quality, and adding logging?

5. No version control.

Mature data science teams use git to keep track of changes to analyses and code. Other teams instead use methods like shared network folders, which don’t let you see when things changed, why they are changed, or previous versions. Occasionally teams don’t share code at all and work just lives on the data scientists individual laptops. Avoid these last groups like the plague. Not having methods of sharing code means the team can’t work together.

Question to ask: how to you share code amongst the team? Is all code shared or just some of it?

6. No clear delineation between people who run reports vs do analyses.

The skillsets required to create and maintain reports, to build data science models, and to put machine learning models into production are all different. If the company doesn’t have a clear way of determining who does what work, you could start your job and end up doing work totally different than what you expected. You don’t want to walk in on your first day expecting to build a time-series forecast and find out your job is to refresh the monthly sales Excel spreadsheet.

Question to ask: how are reporting, analysis, and production-model building tasks split?

Red flags on how they value people

1. A totally non-structured interview process.

A structured interview process means that each candidate gets the same set of questions and can be more equally compared. Not only does [it decrease bias](, it also requires the team think through what’s important in the people they bring on. If the interview process is unstructured, with the interviewer seemingly asking questions off the top of their head, then it’s a strong sign they haven’t figured out what they want in a candidate and how to get it. If they don’t know what they want you are going to have trouble giving them what they want on the job.

Suggestion: see if they bring a set of questions to the interview, or ask the meta-question of how they chose what to ask you.

2. No time for your questions.

Since interviews are also for you to find out about the company, you need to have time to do so. If there isn’t time for you to ask questions, the interviewer isn’t interested in making you feel comfortable and allowing you to assess your fit.

Suggestion: if you get to the interview and you didn’t have time to ask questions, make a note of it and ask the interviewer when would be a good time to ask them instead.

3. No coding required in the interview.

While programming isn’t the most important skill for a data scientist, it is something you will have to do on the job. The coding part of the interview could be on-site or a take-home test, but it should definitely exist. If the interview process doesn’t include programming it could be for a few reasons: (1) The data science team is new so no one can run the interview. In this case, be aware you won’t have support on the job. (2) The team doesn’t have the time to create a programming interview. This is a sign they don’t value hiring. (3) They don’t program and use BI tools like Tableau and Excel for their work. (4) They trust your resume so much that they don’t need to test you, while is flatting but a sign that they are desperate to hire.

Suggestion: if the interview doesn’t include a programming component, ask them how do they tell which candidates have the technical skills for the job.

4. No plan for your first few months.

Companies put job postings out for good reasons. If they aren’t able to clearly articulate exactly what you’ll be doing in the first few months, that reason is probably “we are totally overwhelmed with work and we are going to throw people at the problem until we can handle it.” That’s an extremely dangerous way to grow a team. What’s worse, this usually happens at the companies that don’t have onboarding processes for new hires. So these situations are extremely stressful for the whole team and that usually falls on you too.

Suggestion: ask if they have a clear project and on-boarding process for your start. If they don’t have an extremely precise answer for this, run.

5. No support continuing education.

Data science is a large and rapidly advancing field and if you don’t keep learning you’ll fall behind. Teams should have some way of helping people keep up, whether it’s by providing funding for online education or attending conferences, monthly meetings where you discuss industry blog posts, encouragement to attend meetups or get involved in open source, or a speaker series. This also shows they’re willing to invest in their people generally.

Suggestion: ask how they support the continued education of the team. Is there funding for conferences or workshops?

6. Inconsistent answers between interviewers about the role

Usually interviews have you talking to many people within the company, including your future manager, teammates, and business stakeholders. If they each tell you different things around the level of responsibility, type of work, what the role delivers, and hours you’ll have to work, they themselves probably don’t agree. If they can’t agree, especially on things related to what work you’ll end up doing, your job will end up being full of conflict.

Suggestion: keep track of what people say in different interviews. If you find an inconsistency, ask why.

While these twelve flags may feel like a lot, companies tend to show none of them or most of them, with only few in the middle. By keeping an eye out of them you can avoid the problem of getting to a job you dislike. Good luck!

via Red flags in data science interviews – Towards Data Science

We live on an island surrounded by a sea of ignorance. As our island of knowledge grows, so does the shore of our ignorance.

John Archibald Wheeler

I drank because I wanted to drown my sorrows, but now the damned things have learned to swim.

Frida Kahlo


French Bookstore Invites its Instagram Followers to Judge Books by Their Covers | Colossal

French Bookstore Invites its Instagram Followers to Judge Books by Their Covers

JULY 2, 2018


In addition to laying claim to the title of France’s first independent bookstore, Librairie Mollat has carved a unique niche on Instagram with its #bookface portraits. The Bordeaux-based bookstore regularly features photographs of book covers held up in front of perfectly scaled, dressed, and nose-shaped people (presumably, some are customers, though some repeated faces seem to indicate a few photogenic employees). You can see more from Mollat—and perhaps even get your next book recommendation—on Instagram. If you enjoy this, also check out Album Plus Art. (via Hyperallergic)


via French Bookstore Invites its Instagram Followers to Judge Books by Their Covers | Colossal