$ calendar

Sep 13* Rosh Chodesh Tishrei (Beginning of the month of Tishrei)

Sep 13* First Day of Rosh Hashanah (Jewish New Year; 5768; sabbatical)

Sep 13 Barry Day commemorates the death of Commodore John Barry, USA

Sep 13 Chiang Kai-Shek becomes president of China, 1943

Sep 13 Building of Hadrian's Wall begun, 122

Sep 13 British defeat the French at Abraham near Quebec City, 1788

Sep 13 58� C (136.4� F) measured at el Azizia, Libya, 1922

Sep 13 Walter Reed born, 1851

Sep 14 Francis Scott Key writes words to "Star Spangled Banner", 1814

Sep 14* Rosh Hashanah (sabbatical)

Sep 14 Battle of San Jacinto in Nicaragua

Sep 14 Frodo & Bilbo's birthday

Sep 14 The US Selective Service Act establishes the first peacetime draft, 1940

Sep 14 Salem, Massachusetts, is founded, 1629

Sep 14 Benjamin Franklin is sent to France as an American minister, 1778

They've saved me from some bad gigs.

The second half of the video covers questions for your would-be future managers. Some of the commenters have also added excellent questions to ask.

Here are the co-worker questions:

1. What's the most interesting thing you've gotten to work on (or learn about) here?

2. What's the balance between firefighting and project work in an average week?

3. What's the one thing you wish you could improve or change about your everyday work life here?

4. Every company is carrying some amount of technical debt -- what's the tech debt situation here? -how much they think there is -what they're doing about it

4. What is the policy/practice as far as WFH or remotely? 5. What does the on-call rotation look like?

I think of Udemy as the Packt of online course platforms. There are certainly some gems to be found, if you have the time to look for them and don't mind getting a few duds in the process.

1. I have a friend with a well performing Udemy course and he's doing about the same price/unit sale as you are. I think it's just a volume thing.

2. Udemy courses are so inexpensive and as a result does it matter if there are a few duds? Again from the stats my friend shared, about 80% of the people buying the course don't even start it.

And just to clarify, I didn't mean to sound negative. I actually think it's a good thing -- I've picked up several really good courses on real estate investing, machine learning, and other stuff.

A few misses at $10/pop come out in the wash when you also get courses that can add tens of thousands of dollars' worth of knowledge to your life.

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2. Where do you see the $(team you're interviewing for) in 2 years? - do they respect the team or are they looked at as an annoying expense? - do they talk about training opportunities, growth, etc?

3. What is your favorite aspect of working here? - If it's a stock bullshit answer, I dig more. - If they duck it, that's a smell. - If they answer honestly, then that's a really valuable datapoint.

4. Every company is carrying some amount of technical debt -- what's the tech debt situation here? - how much they think there is - what they're doing about it

5. How would you describe the culture and if/how it is maintained?

5a. Is there an active mentoring strategy? What does that look like?

I made a video about this a while back with a few more questions, and some people left useful comments on it with their own experiences/additions: https://www.youtube.com/watch?v=s9XPTay-x8g

1) If you could change one thing about this place by waving a magic wand, what would it be? (ties in with above but more open ended)

2) Who do you see as your competition?

3) What do you hope to gain by hiring somebody?

4) Why isn't this role being filled from within the company?

If things had improved that much in NLP-land since I last played around with it, I would jump careers right now :-D.

We've had a pretty splendid experience with Vault so far, so I'm not exactly in the market for another solution, but this looks interesting.

I assume you started development on this because Vault et al didn't exist yet at the time? Or was there another driver?

$1 per group of secrets, e.g. a service. In many cases I think this is fine, but it is certainly not a strength.

this will be the blog (at some point). The overall idea of the dissertation was to see if combining different ways of processing the text of the reviews (classifiers using features from analysis of the grammar, vocabulary, etc) into a custom heterogeneous ensemble was better than using one classifier and the traditional ensemble creation methods (AdaBoost, bagging, etc). I figured creating a more holistic view of the text would be better; other studies have done this, but not to the extent I did. And I analyzed exactly why things did or did not work.

So it was just fundamentally a exercise in NLP; I did not use other signals like the # of reviews submitted in one day or other things like that. My gut says this general idea (a more holistic view) would apply to classifying other text, like fake news. But proving that is yet another project.

I still have a couple more angles (dependency and constituency parsing, framing) to add to the mix, so I'm not totally done. It will be a long series of blog articles. And I ended up having to deal with the problem of diversity vs. accuracy, so the dissertation went down a side road. My supervisor said it could be two potential papers for publication instead of one... At least I won't be bored for the next year.

Thanks for your interest! If you send me your email (dfraser@... is mine), I can send you the PDF, or pointers to other info about the research into fake reviews in general (e.g. using other signals like # of reviews/day); I'm not going to get the blog up soon - already dealing with a ML project for Network Rail here in the UK.