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jaredchung

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Your cancel journey is incredibly inspirational and I will share it on.

But actually, I'm writing to tell you that many of the OTHER things you have done have had a huge positive impact. Gitlab foundation, for example, has been extraordinarily impactful on our organization and many others.

This is the way!

Edit: I did it. Paid them $0.51 to clean room `copyleft`, just to see what would happen. A clean package is now sitting on my desktop, custom-built (I presume) and fully documented. Deleting it now, for obvious reasons. But is it still satire if they actually provide the literal service they're satirizing?

How far do they take the satire? If you pay them do they actually generate output?

I'd be interested in analyzing ATC transcripts to quantify the frequency of communication errors like that in day-to-day operations. Express permission, followed by an explicit (and matching) read-back, to enter a runway should be easy to identify (and the opposite as well). I see liveatc.net has audio archives, and tailstrike.com has transcripts of select accidents.

CareerVillage.org | Software Engineers | REMOTE | Full-time

Tech for good: We're a tech nonprofit building an AI Career Coach for education and workforce development customers. We are a well-funded, remote-first 501c3 that has been developing large-scale career platforms for underrepresented youth for over a decade. We've served millions of young people, and are an early mover on AI within the nonprofit space. The stack is Django, Langchain, React. AWS and GCP. Multi-LLM.

We have four software engineering positions open: https://boards.greenhouse.io/careervillage

If we believed ChatGPT was the cause of an SO decline, then I'd expect it to also cause a decline in Google searches for programming queries. Here's the Google Trends data for two super-common error codes and one more conceptual programming search query:

https://trends.google.com/trends/explore?date=today%205-y&q=...

Maybe a little something going on with typeerror? Or just noise. But it definitely doesn't look to me like evidence supporting the article's claim.

That's not constructive. Please refrain from attacks like this on HN. AWS Organizations is changing the name of the “master account” to “management account”. It makes sense for OP to update their docs accordingly, to remain consistent with AWS's naming system.

Several.

FastForward is probably the largest and furthest along in organizing around Tech Nonprofits specifically, with a pretty solid base of alumni success stories(https://www.ffwd.org/).

I'd also look at Visible Connect, which is tech nonprofit focused. Full Circle Fund, if you're in the Bay Area. Since you mentioned you're in Education, you should also look at the Aspire Accelerator, which isn't exclusively tech nonprofit but includes many tech nonprofits.

You could also consider YC which has taken a few nonprofits.

It's GREAT that you're in this position! CONGRATULATIONS!!!

I went through this as well. 10 years ago I was at the peak of a career but missing purpose. I rededicated my life to serving other people. It has given me a whole new lease on existence, and I can't recommend it enough. It really feels better to give than to receive!

The short, overly simplistic answer is that all you need to do to rededicate yourself to a life of helping people is to listen and learn to what they need. Volunteering is a good way to learn about people who need help, but it's not the only way. Volunteering in person can be tough right now because of COVID, but there are some things you can do online, and if you have a bit of patience you could wait until in-person volunteering picks back up again and then establish some goals for yourself (e.g., "volunteer at least 3 days each month for 6 months")

Outside of volunteering, you could take time to listen to and learn about people who you think might need help. Depending on your geography, those might be different folks.

If the things above STILL don't work for you, you could do something that is maybe slightly more dangerous, but still can work. Flip it around and look for things to be outraged by. Things you can't stand. Mass deforestation. Racial injustice. Human trafficking. 1000 other injustices to choose from. And then get active! (Be cautious with this one -- rage alone isn't productive -- you have to mix your outrage with your proactive strengths and positive energies too!)

In my case, I ended up discovering my passion for helping low-income youth prepare for careers, which became my full-time focus and it's been a true privilege to work on.

Well yes, if you constrain the solution set to satisfy "requires no additional software to learn" then you'll end up with the status quo solution.

One thing I've always been concerned about when negotiating a legal agreement is how I verify that the tracked changes actually track every change. Because Word lets the user decide which changes to track, I'm always reading the untracked sections as well to confirm that no other changes were sneakily introduced. That's something that git addresses well. Does Word have a solution there? If not, does that ever concern you?

We just implemented topic suggestions as well on our Q&A data. The literature in this field is actually quite solid. Here are a few key insights from our research: 1) Using recall@5 as your test metric gives you the benefit of being able to compare your results against the academic literature. They often (but not always) use recall@5. 2) We read the articles that introduce the following systems: TagCombine (was state of the art in 2013), NetTagCombine, EnTagRec, TagMulRec, fastText's supervised topic recommendation, and a few others. Unfortunately other than fastText not many of these have OSS libraries available out of the box, but that's ok as long as you're willing to use the underlying methods which are available off the shelf. 3) Our approach: we tested a few of the above systems, as well as mix-and-matched our own systems with tf-idf, topic modeling, multilabel classification, l-lda, fasttext, and others. For each attempt we calculated (a) recall@5 against our test set (we used k-means cross validation) and (b) how long it took to complete the training step. 4) In the end, we decided that we got the best combination of recall@5, training time, and engineer efficiency by using ONLY the fastText library. In the end we spent about 1 week trying other methods before we tried fastText. fastText took us about 3 hours to get first results, and then we tweaked the parameters for another two days before we found the right combination of learning rate, epoch, n-gram #, etc. (read the docs :) Our current recall@5 for v1 of this feature is a little above 0.5, which we think is good enough to provide a solid user experience.

Caveat: Unlike Github we actually do have user-generated labels to start with, so our modeling problem has one-step less complexity than theirs but are otherwise the same.

(TL;DR If you have gold standard data to start with and no time to read academic articles, try fastText supervised learning. If you have time to read academic articles, start with a lit search.)

If we're talking about alternatives for giving money to a charity, I strongly recommend opening up the contact page of your preferred charity, calling them and just asking them what method they would prefer for you to send them money. Charities often have preferences that allow them to save money on transaction fees. For example they might have a discounted rate with a particular credit card company or prefer a physical check, or want you to pass it through a fiscal sponsor. Especially for small charities -- donors are the lifeblood of charities, so they will bend over backwards to help you give!

Source: I run a charity.