Comments URL: https://news.ycombinator.com/item?id=47697904

Points: 28

# Comments: 4

Usually these people are knowledgeable because they are constantly learning. Eventually they become wise.

What do you think of that definition?

Having a high IQ isn't enough - you must also choose to be happy.

Was this dynamic on your radar when building the game and camera system? Would love to hear your thoughts.

Comments URL: https://news.ycombinator.com/item?id=45069335

Points: 3

# Comments: 1

What was the point of this judgmental comment?

Comments URL: https://news.ycombinator.com/item?id=44871488

Points: 3

# Comments: 1

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"Within the last few weeks, Mark and I have built and launched Offline.Kids.

It’s a website to help parents reconnect with their kids and for kids to reconnect with the world around them.

Offline.Kids is directory of screen-free activities for all ages. Each activity is categorised so that parents can find appropriate activities for their situation.

For example, you can find:

quick, clean activities for a 6 year olds outdoor kids activities that take 1-2 hours low energy indoor crafts We built the site off the back of our new directory landing page plugin (catchy name still in progress!). It instantly creates thousands of SEO friendly landing pages for the activities. It’s early days, but Google is successfully indexing the pages and we’ll see how the rankings change over time.

So, if you’re looking for screen-free activities for your kids, check out the website, and share with anyone you think might find it useful!"

Criticize the process of creating it all you want.

The issue they have is with low-quality content. If the AI generated content was better than most human-created podcasts and were making their engagement numbers go up, I doubt they would be calling them fake or removing them.

OpenAI has said they are working on making ChatGPT's output more configurable

Examples I found interesting:

Semantic map lambdas

  S = Symbol(['apple', 'banana', 'cherry', 'cat', 'dog'])
  print(S.map('convert all fruits to vegetables'))
  # => ['carrot', 'broccoli', 'spinach', 'cat', 'dog']

  # Contextual greeting comparison
  greeting = Symbol('Hello, good morning!')
  similar_greeting = 'Hi there, good day!'

  # Compare with specific greeting context
  result = greeting.equals(similar_greeting, context='greeting context')
  print(result) # => True

  # Compare with different contexts for nuanced evaluation
  formal_greeting = Symbol('Good morning, sir.')
  casual_greeting = 'Hey, what\'s up?'

  # Context-aware politeness comparison
  politeness_comparison = formal_greeting.equals(casual_greeting, context='politeness level')
  print(politeness_comparison) # => False

  # Semantic logical conjunction - combining facts and rules
  horn_rule = Symbol('The horn only sounds on Sundays.', semantic=True)
  observation = Symbol('I hear the horn.')
  conclusion = horn_rule & observation # => Logical inference

OP, what inspired you to make this? Where are you applying it? What has been your favorite use case so far?

Would reduce max score to 400M and you'd have to round 0.25 up or down. Would probably want to drop the 0.01 cards too.

super cool project

My guess is the prompt was something like this:

"describe a color palette generating tool that uses the magic of polar coordinates to make pretty color palettes"

Great addition regardless. Good to see open weight model progress.