Comments URL: https://news.ycombinator.com/item?id=41945366

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=41925278

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=25199971

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=24311260

Points: 74

# Comments: 21

It was a fun project; I learned a lot about how the CPython implementation works and have a lot of respect for the people that built it. It was surprisingly easy to implement Tauthon based off the work the core dev team did on Python3: https://www.naftaliharris.com/blog/nonlocal/

For what it's worth, I do believe that Python3 is a better language than Python2. We use Python 3.7 at my work (SentiLink) and we've had a good experience with it. (If you're starting a new project or can migrate, I'd recommend it). But I do think that the ~10 year saga of upgrading to Python3 from Python2 wasn't necessary when the main benefit was really the unicode refactoring.

I no longer maintain Tauthon personally but there are others who are excited about the project who occasionally add new features or bugfixes.

SentiLink prevents synthetic fraud, an emerging fraud vector in which fraudsters open accounts using name/DOB/SSN combinations that don't correspond to real people. Our partners include top ten US banks, fintechs, and alternative lenders. We're backed by investors including Andreessen Horowitz, Max Levchin (Affirm CEO/PayPal Co-Founder), and former presidents/CEO's of Visa, Transunion, HSBC, and Citi.

We recently closed a $14M Series A [1] and are hiring software engineers to help us build our identity platform. Our tech stack uses Go (for the API part) and Python (for the ML part) on k8s and the work involves a lot of complex and sensitive data.

Please apply at https://jobs.lever.co/sentilink.

[1] https://businessinsider.com/synthetic-fraud-detection-startu...

SentiLink prevents synthetic fraud, an emerging fraud vector in which fraudsters open accounts using name/DOB/SSN combinations that don't correspond to real people. Our partners include top ten US banks, fintechs, and alternative lenders. We're backed by investors including Andreessen Horowitz, Max Levchin (Affirm CEO/PayPal Co-Founder), and former presidents/CEO's of Visa, Transunion, HSBC, and Citi.

We recently closed our Series A [1] and are hiring software engineers to help us build our identity platform. Our tech stack uses Go (for the API part) and Python (for the ML part) on k8s and the work involves a lot of complex and sensitive data.

Please apply at https://angel.co/sentilink/jobs or shoot a resume/github/linkedin to me, (my first name at a domain I'm sure you can guess).

[1] https://businessinsider.com/synthetic-fraud-detection-startu...

1. Unlike with ID theft, there's no consumer victim. With ID theft, eventually the victim will find out about it, (by getting a call from a collections agent or seeing the trade on their credit report). They'll then contact the lender or the bureau, and contest the validity of the loan. The end result is that the lender gets a stream of loans that are labeled as identity theft losses. Since there's no consumer victim with synthetic fraud, though, lenders don't get this stream of labeled data and have a hard time knowing which of their losses are synthetic fraud (and which are just ordinary credit losses).

2. Synthetic fraud cuts right through typical ID theft prevention systems. ID theft prevention is about checking whether the applicant is the same as the identity they're using to apply for credit. So you check if the email the applicant uses matches the identity, (e.g. don't want john.doe@gmail.com used as the email for Jane Smith), you check the phone number, you check if the applicant can complete KBA (knowledge based authentication, e.g. questions about previous addresses), you check the billing address, and so forth. But synthetic identities have their own aged phone numbers, emails, addresses, and credit histories, and so all of these verifications go through without any flags raised. Essentially the ID theft prevention system was checking whether the applicant is the same as the identity that they're using, but with synthetic fraud the applicant created the identity.

Source: my startup focuses heavily on preventing synthetic fraud for lenders, (PM me for details).

SentiLink is reinventing identity, beginning with financial services in the United States. The current system is broken: SSN's are used as both a username and a password, but after repeated data breaches are also effectively semi-public. Identity-verification data isn't shared, so the same fraudsters target every company and consumers have to continually reverify themselves with different institutions. Billions of dollars are lost every year to criminals who are very rarely caught or punished. SentiLink is building the arbiter of identity to bring identity into the 21st century.

Our investors include former co-founders and C-level execs at PayPal, Palantir, Affirm, Visa, and Citibank, including Max Levchin (SciFi) and Hans Morris (Nyca Partners).

Apply here: https://angel.co/sentilink/jobs or email me (first name at sentilink.com).

That said, I've got to imagine that claims that "as many as 20 percent of the students in one 2015 computer science course were flagged for possible cheating" are a misrepresentation or a misunderstanding, on the part of the journalist. I mean, sure, if you set the threshold for the plagiarism detector at a low level, you can flag 20%, 50%, or however many students you want for "possible cheating", but it's not necessarily a real thing.

Comments URL: https://news.ycombinator.com/item?id=14186986

Points: 10

# Comments: 6

[1] https://projecteuclid.org/euclid.aos/1176345691

Even more crazy, the James-Stein Estimator which does this actually uses data about the football player and soccer player to make predictions about the baseball player, (and vice-versa). This is deeply unintuitive to most people since the players aren't related to each other at all. The phenomenon only holds with at least three players; it doesn't work for two.

(More generally, Stein's Paradox is the fact that if you have p >= 3 independent Gaussians with a known variance, you can do better in estimating their p-dimensional mean than just using their sample means).

I've spent a bunch of time trying to understand why this actually works [2]; to be honest I still don't deeply understand. But nonetheless the consensus is that the same shrinkage phenomenon is what causes improved performance for a variety of high-dimensional estimators, (lasso or ridge regression, e.g.), making the paradox very very influential.

[1] https://en.wikipedia.org/wiki/James%E2%80%93Stein_estimator [2] https://www.naftaliharris.com/blog/steinviz/

So I expect that a big factor in whether quality local newspapers can survive is the strength of the local housing market, (measured through e.g. median house price and yearly volume). As a practical matter, this means that only in relatively affluent places is local news financially feasible, (although the housing market isn't the only reason why that's the case). It also means that more people searching for property online may present a challenge for local news.

[1] http://www.paloaltoonline.com/morguepdf/2017/2017_03_24.paw....

Comments URL: https://news.ycombinator.com/item?id=13923867

Points: 1

# Comments: 0

[1] https://www.creditkarma.com/tax

Comments URL: https://news.ycombinator.com/item?id=13868956

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=13815811

Points: 1

# Comments: 0

It lets people with Python 2 code start to use new features from Python 3. (It's a backwards-compatible fork of Python 2.7 with features like async/await, function annotations, and keyword-only arguments backported from Python 3).