rovyko

The cold call approach is something I've been hearing a lot. It's scary to just start talking to people when you have no experience, but I think that's just life. It was the same with negotiating salary, learning new thing and meeting new people. You just need an objective goal, courage and a way to learn from failures.

Thanks for your input!

In psycopg2, variables can be inserted into queries using %s placeholders and supplying arguments to cursor.execute, but this does not allow for identifier arguments such as table or columns names. The alternative is to use psycopg2.sql.SQL.format, but this requires arguments to be converted into Composable objects such as Literal or Identifier.

With TemplateQuery, instead of wrapping arguments with psycopg2.sql classes (e.g. Literal, Identifier) the expected class can be written inside the query:

  >>> TemplateQuery('SELECT * FROM {table@Q} WHERE {@I} {@S} {value@L}').format(
  ...    'column_name', '>=', table='public.my_table', value=100
  ... ).as_string(conn)
  
  'SELECT * FROM "public"."my_table" WHERE "column_name" >= 100'

The character after the at sign @ in the placeholder key is the transformation that is applied to the argument before inserting it into the placeholder (S for raw SQL, I for Identifiers and L for literals, among others).

I wrote this small library while working on my master's thesis to help run similar analysis queries on different tables and schema configurations, without the mental overhead of wrapping arguments in the proper classes. I'm not sure if it will be useful to anyone, but I thought it was worth releasing. Would appreciate any feedback at all!

>If you are a computer programmer considering a career change you should really take a day off to look into the new genetic technologies on offer.

What resources would you recommend, and do you have any advice for someone who is just starting their career?

This would make a great visualization for a bassy music video. Link variables defining the polygon shape with amplitudes for bass frequency ranges and the color to the vocal tone.

I wouldn't mind a shared bathroom or kitchen, but I don't understand the massive living rooms. My guess is it makes the unit more marketable to higher income students that in a lower volume can pay more in total for a given plot of land.

You got it. I was lucky to start when it was affordable. The residence fees this year are completely insane. $1000/mo for a single dorm room and even more for a room in a 3-4 bedroom apt. Off-campus housing isn't much better.

Rent definitely affected my choice of school. With identical offers, I opted for a smaller tech-oriented university over the University of Toronto, which is either the top or one of the top schools in the country. I don't regret it, because rent in Toronto was insane and it's only getting worse. I'd have to commute 90 minutes to my parents' house with no time for a social life, and I'm super lucky to know that I even had that option.

And now rents in the small town are growing. Subdivided houses are torn down for luxury apartments, and with recent cuts to government loans, it might not be affordable for some low-income students.

>As part of our staged release strategy, our current plan is to release the 1558M parameter model in a few months, but it’s plausible that findings from a partner, or malicious usage of our 774M model, could change this.

This seems naive but I think it's a misdirection. Of course the model will have malicious users. Propaganda teams started testing its integration as soon as it was released. It's likely that OpenAI is counting on this for insights into HOW the model can be used maliciously. It's also possible that the model results have inherent trackable markers and OpenAI can later say that X% of social media posts were made using this model.

So what are the positive applications, aside from prettifying data like sports and weather reports?

Even with Skyrim's 800+ books, you frequently ran into the same book. Imagine libraries filled with plausible text that hides nuggets of lore seeded by developers. Along with more realistic text-to-speech this can allow games to support a large diversity of NPCs that have true radiant dialogue and sound more realistic than "I saw a mudcrab the other day".

With some modifications, I think models like this can outweigh even their nefarious applications:

Defense against text decomposition analysis. The model can be used to obfuscate writing patterns that can reveal a person's identity, either by randomizing form or standardizing it. Take your post and run it through the formatter to get the same idea and intent, but in a style that can't be traced to your other writing. Or you reform it into style of Ernest Hemmingway, like thousands of others.

Realtime plausible deniability encryption. Messages in a monitored chat can look like mundane conversation but contain encrypted messages. This would require the model accept seeds and work partially in reverse to diff two sets of text to reveal the hidden message.

In it's current form it doesn't look like it can do any of those things, but there's the potential.