This "blog post" appears to just be copy-pasted content from the NASA article [1]. I give credit for the source being cited, but it's still plagiarism.
There is absolutely nothing wrong in copying from a government website. They're in the public domain, and for good reason. In fact, citing the source isn't even necessary.
For plagiarism to apply, the source must not have been cited, and the source must have been copyrighted, neither of which apply here.
This is a pet peeve of mine and I'm glad to see it called out. That said, I haven't seen a comprehensive discussion of "here's the different factors that we think contribute to creating lift" for the general public, is anyone aware of a good source?
> This theory also does not explain how airplanes can fly upside-down (the longer path would then be on the bottom!) which happens often at air shows and in air-to-air combat.
While true, the person writing this article does not seem to understand the difference between flying inverted and flying with a negative angle of attack. These can happen at the same time, but not necessarily. If you're performing a loop or a barrel roll, you will be inverted, but the aircraft will be performing largely as it would be when you are straight and level, because you are still under positive g with a positive AOA on the aircraft. The lift vector will just be pointed someplace other than "up."
This is the problem with LLMs, they return common knowledge as fact.
Interesting that will all the Ph.D. expert fine-tuning that GPT5 supposedly received, it still doesn't favor the more correct Newtonian explanation of airplane lift.
To me the whole demo [edit: today's openai live stream] didn't feel revolutionary at all.
Especially the code generation part. It feels to me like Claude Web can do those illustration artifacts already for months equally well.
Also the example in Cursor just felt like a regular Claude Code session, just with different UI.
The only part I'm excited about is, that there is no distinction between reasoning and non-reasoning models anymore. I tend to default to reasoning models, because too often I feel like I need to switch mid-conversation to a reasoning model anyway. And reasoning models degraded the user experience drastically, because it often takes them quite some time to start responding.
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[ 2.7 ms ] story [ 27.7 ms ] threadthen it then went away and generated a load of confidently incorrect total bullshit
"phd level" my backside
[1] https://www.grc.nasa.gov/www/k-12/VirtualAero/BottleRocket/a...
For plagiarism to apply, the source must not have been cited, and the source must have been copyrighted, neither of which apply here.
While true, the person writing this article does not seem to understand the difference between flying inverted and flying with a negative angle of attack. These can happen at the same time, but not necessarily. If you're performing a loop or a barrel roll, you will be inverted, but the aircraft will be performing largely as it would be when you are straight and level, because you are still under positive g with a positive AOA on the aircraft. The lift vector will just be pointed someplace other than "up."
Naturally. This is how LLMs work. It regurgitates the data fed into it.
https://www.youtube.com/watch?v=1GAp2dlIC8I
Interesting that will all the Ph.D. expert fine-tuning that GPT5 supposedly received, it still doesn't favor the more correct Newtonian explanation of airplane lift.
Especially the code generation part. It feels to me like Claude Web can do those illustration artifacts already for months equally well.
Also the example in Cursor just felt like a regular Claude Code session, just with different UI.
The only part I'm excited about is, that there is no distinction between reasoning and non-reasoning models anymore. I tend to default to reasoning models, because too often I feel like I need to switch mid-conversation to a reasoning model anyway. And reasoning models degraded the user experience drastically, because it often takes them quite some time to start responding.