Oh, look, another riveting chapter in the epic saga of "Why Nobody Can Get Rust
#Lifetimes Right" 🤦♂️. This masterpiece claims to untangle the infinite spaghetti of
#misconceptions, but spoiler alert: you're still lost in the sauce. 👨🍳🍝
https://github.com/pretzelhammer/rust-blog/blob/master/posts/common-rust-lifetime-misconceptions.md #RustProgramming #CodingHumor #DeveloperStruggles #HackerNews #ngated
rust-blog/posts/common-rust-lifetime-misconceptions.md at master · pretzelhammer/rust-blog
Educational blog posts for Rust beginners. Contribute to pretzelhammer/rust-blog development by creating an account on GitHub.
🎉🚀 BREAKING:
#GraphQL isn't the magical unicorn you thought it was, dear
#enterprise warriors! After years of "real" use, our hero discovers it's just another tool that doesn't fit everywhere—shocker! 🙄 If you're expecting sparkles and rainbows, you might want to look elsewhere...🌈✨
https://johnjames.blog/posts/graphql-the-enterprise-honeymoon-is-over #Misconceptions #Tools #Reality #Check #Software #Development #HackerNews #ngated
GraphQL: the enterprise honeymoon is over
A production-tested take on GraphQL in enterprise systems, why the honeymoon phase fades, and when its complexity outweighs the benefits.
LLMs Hallucinate: Why AI Explanations Are Often Made Up | Britney Muller posted on the topic | LinkedIn
Stop asking ChatGPT about how it works (it's making stuff up)
We've all seen this: Someone gets a weird answer from an LLM and asks, "Why did you say that?" The LLM replies, "I mentioned X because of Y..." And the user thinks they've uncovered some underlying LLM logic.
The model is just confidently hallucinating its own 'reasoning'.
LLMs are not decision trees. They are not SQL databases that you can query. They are not logging anything internally to read back to you. They are not truth engines.
They are probability machines.
When you ask a model "why did you answer in that way?" the model isn't looking back at its own architecture or code, it's just predicting: [What would a helpful AI assistant say in this situation?]
This is called Post-Hoc Rationalization: inventing a story/reason that fits or justifies the answer it just gave you.
Not to mention, LLMs are tuned (via RLHF) to be helpful and agreeable. -Sycophants by design, telling you what they think you want to hear.
Best analogy I can come up with for these sycophantic tendencies: Ever asked a kid who has chocolate all over their face, "Did you have a cookie?" (when they weren't supposed to)?
They'll think up & give you an answer that's most likely to make you happy (and keep them out of trouble). "The dog ate one!"
The big difference? Kids know the truth! LLMs don't. They just invent one.
Note: I want to applaud everyone testing these tools; it's the most powerful path to learning! It's one thing to read about this stuff and an entirely other thing to experience it. Be kind to yourself. Be kind to others & Stay Skeptical!
(edit to improve my 🍪 analogy) | 179 comments on LinkedIn
Common misconceptions about disability - TetraLogical
Disability is often misunderstood or misrepresented online and in mainstream media. In this post, we look at some common misconceptions surrounding disability.
Your Perfectionism is Lying to You
Unpacking the counterintuitive approach that actually drives creative success
A study followed 'gifted' kids for over 50 years. Here's what we've learned about them.
https://fed.brid.gy/r/https://www.upworthy.com/genius-students-ex1
You Can’t Make Something Accessible to Everyone
This post’s title is unpleasant, but it’s important to acknowledge the reality of the human condition and limitations in technologies. Even purpose-built assistive tech. Broadly, when someone says something is “accessible” that’s a hopeful statement that is based on some best efforts. Of course, there are bad actors who assert…
N-gated Hacker News
Biohacking Pathway
UselessFactsBadlyDrawn
Useless Facts, Badly Drawn
Inautilo
Sharing the best of humanity with the world, one story at a time.