521–530 of 1001 posts
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can you ask it: 9.11 and 9.9, which number is larger
4.9 is larger than 4.11. Explanation: • 4.9 is equivalent to 4.90. • 4.11 remains 4.11. When comparing the numbers: • 4.90 (which is 4.9) • 4.11 Since 4.90 > 4.11, 4.9 is the larger number.
Pricing is $15.00 / 1M input tokens and $60.00 / 1M output tokens. Context window is 128k token, max output is 32,768 tokens.
There is also a mini version with double the maximum output tokens (65,536 tokens), priced at $3.00 / 1M input tokens and $12.00 / 1M output tokens.
The specialized coding version they mentioned in the blog post does not appear to be available for use.
It’s not clear if the hidden chain of thought reasoning is billed as paid output tokens. Has anyone seen any clarification about that? If you are paying for all of those tokens it could add up quickly. If you expand the chain of thought examples on the blog post they are extremely verbose.
https://platform.openai.com/docs/models/o1 https://openai.com/api/pricing/ https://platform.openai.com/docs/guides/rate-limits/usage-ti...
It failed ;)
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Software engineering contains a lot more than just writing code. If we somehow get AGI, it'll change everything, not just SWE. If not, my belief is that there will be a lot more demand for good SWEs to harness the power of LLMs, not less. Use them to get better at it faster.
I don't think anyone is worried about SWE work going away, I think the concern is if SWE's will still be able to command cushy salaries and working conditions.
It's very important to human progress that all jobs have poor working conditions and shit pay. High salaries and good conditions are evidence of inefficiency. Precarity should be the norm, and I'm glad AI is going to give it to us.
Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?
Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?
LLMs cannot decide what to work on, or manage large bodies of work/code easily. They do not understand the risk of making a change and deploying it to production, or play nicely in autonomous settings. There is going to be a massive amount of work that goes into solving these problems. Followed by a massive amount of work to solve the next set of problems. Software/ML engineers will have work to do for as long as these problems remain unsolved.
The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…
This does not make the state of things any less ridiculous, however.
Sounds great, but so does their "new flagship model that can reason across audio, vision, and text in real time" announced in May. [0] [0] https://openai.com/index/hello-gpt-4o/
That is in chatgpt now and it greatly improves chatgpt. What are you on to now?
So no, it's not in chatgpt.
Sounds great, but so does their "new flagship model that can reason across audio, vision, and text in real time" announced in May. [0] [0] https://openai.com/index/hello-gpt-4o/