ginger_beer_m

This thread seems hidden in hackernews, you might want to repost this AMA on reddit? In the right sub it will get a lot more attention.

As for my question, I'm curious of what the tech stack were at that time, and if you think we can finally produce that in real time now? Including even crossing the uncanny valley which I think is still challenging even now 25 years later.

seamless latex integration is a winner for me!! will definitely spread the words for this

Tiny context window is killing it.

Would you share the salary range?

It would also be interesting to know the reverse question: anyone ever got a job from who's hiring thread?

Did you spend as much on product development too?

Hi when you say 'San Carlos, CA / Remote', does it mean we have to be in San Carlos, CA but able to work remotely? Do you accept applicants from outside the US?

Very interesting roles! Do you have any remote from UK opening?

Mine cryptocurrency is the obvious answer.

Once you're done building a working system to do the above, you'd have basically built a blockchain.

Could you share what are these bio hacker spaces, for example?

Can it be used to call numpy, pytorch etc? If not, it's useless

I'm interested to build something like this too. Would you open source it so we can collaborate? It's surprising how few OSS alternatives exist for low-code programming.

For chess application i mean

I bet if you benchmark your n-gram vs GPT-2, it will perform the same.

Nice solution, but there's no way I would touch it unless it's open source. Any plan for that?

It's a running joke in the channel. "Hi welcome to my channel TechLead, with your host The TechLead who's an ex-google and now an ex-facebook tech lead"

It's useless because the core machine learning part is implemented in JavaScript (nodejs). Problem is most of the state-of-the-art codes out there are done in python.

I keep this in mind:

'Starting an idea is easy. What matters is finishing it.'

Check out https://www.openmined.org

They could choose either to retain the funds as (stable) crypto or sell the coins in order to withdraw USD to their bank account. It's all about choices.

Take the volatile crypto (e.g. bitcoin or ethereum) and immediately sell them for stablecoins like DAI to retain the buying power. https://medium.com/@james_3093/the-dai-stablecoin-is-a-game-....

Anyway if the fundraising is over a short period of time, maybe even a volatile crypto might do the job.

If you accept crypto-currency, they don't need to wait 3 days until they see the money in the bank account.

Care to elaborate why? Does the increased pay make up for it?

I did an Msc in Bioinformatics before continuing to a PhD in CS. I'd say that your assessment is largely correct. In general, I'd advise anyone to go into a CS masters. You can always jump to a bioinformatics job without much difficulty, but the opposite move isn't always true. The other replies have also noted that there's a lot of bioinformaticians who are basically scripters. Their jobs consist of running ad-hoc analysis scripts for other people. It's okay but not the most rewarding in terms of job satisfactions, because of point (4) in your post. The other posted who mentioned that the source of funding often comes from short-term grants is also correct in my experience. This means your job tend to be low-paid and last only as long as the grant duration. Altogether I must say, if you're happy in data science stuff, stay where you are. I'm making the move out from bioinformatics as well.

No group studies AI because it's an unproductive use of research time, and also it's hard to write proposals to raise funding when the goal is ambiguously defined.

How is it like working at Amazon? Is the new york times article about the poor working condition in Amazon applies to the UK side as well?

What kind of information do they gather, and can that be automated?

What are the hallmarks of bad R codes to watch out for and avoid?

Can it do anything that can't be done locally using off-the-shelves packages like nltk etc?