I don't think it is cut and dry as that. Of the top of my head I can think of "Jorge Luis Borges" who was a voracious reader and much of his career involved reading (literary adviser, librarian etc.). I don't think (can't know for sure) he hated his job.
I think "Arrival" as a story is better than the movie. I think the movie misses on the part on how hard communication can be, and how different is the way aliens grok the reality as a whole. Also did you watch the movie first and read the story or the other way. I read the story first and then watched the movie with lot of anticipations, and was tad disappointed.
> I think "Arrival" as a story is better than the movie. I think the movie misses on the part on how hard communication can be, and how different is the way aliens grok the reality as a whole.
The movie does not miss anything about the difficulties of communication because that is not what the movie is about: it is about motherhood/parenthood, love, grief.
That is the point I am making: how hard communication is, grokking reality as whole are some of the core themes of the story (is what I felt). The movie doesn't focus on these aspects at all, maybe these are hard to adapt in a mainstream Hollywood movie.
The article do mentions why they don't use multimodal retrieval. Also I think this approach is cheaper (compute wise) than multimodal retrieval. From the article:
Multimodal retrieval does not suit this domain. CLIP-style embeddings wash out exactly the fine detail that matters in charts, tables, and annotated screenshots, and short technical queries ("how do I configure X") give too little signal to match against image vectors
What if the team they're interviewing for doesn't has any parents. I think probably it is fine to ask about their life/interests outside of work. But if the interviewee isn't comfortable answering those it is better not to push
I don't get it, why would operating a datacenter needs massive amount of high skilled blue-collar labor. Datacenters are resource hungry. With so much automation in place I don't think there would be a need for large pool of labor.
In my effort to better understand Deeplearning I built this project (https://github.com/sateeshkumarb/anomaly_detection) to detect
anomalies from a batch of loglines. I use 1D CNN and Siamese network (Triplet loss) to train the model to learn anomaly patterns from logs.
The goal was to detect anomalies that emerge across multiple lines (e.g., error bursts) rather than just single-line keywords.
The approach seems to work on synthetic dataset (with ROC AUC score: 0.9957) but couldn't try it out in a real world dataset. Seeking feedback on the approach.
As someone who doesn't know/watched "Mean Girls" this seems confusing. Also I don't think it is fair to assume that reading of a blog post makes one to understand Raft better, at best they pique one's interest to learn more about it. IMHO Reading/re-reading of the Raft paper and working through an instructional implementation like (https://github.com/eliben/raft) provides a better understanding.
It’s not explaining any deep technical details. Think of it as a gentle introduction to the idea for someone to explain why they might want to read the paper, before reading the paper.
If the article was intended to be an intro to "Raft" algorithm I would've agreed with your sentiment. But this is what the article starts with:
Understanding Raft can be tough. In fact, I’ve seen conversations recently on social media in which actual technical leaders of infrastructure companies demonstrate a lack of understanding (!). Point being, you’re not alone.
Anyway I now notice that the article was written in 2023, probably I'm being too pedantic.
I think the point being made (I’m not the author after all) is not that they are misunderstanding how Raft achieves consensus, but what it means in the first place when we speak of it doing so and why that’s useful. By “technical leaders” here, one might think of CTOs, directors, and senior managers over technical teams rather than senior ICs.
https://jvns.ca/
Not a tech. company blog. Explains technical concepts clearly and top notch technical posts. Fits 1,2, 3 criteria of what you ask, though not the 4th one.
Totally. She has a fearless approach to learning complex topics (my favourite quote is simply "Computers are knowable", though I couldn't find it — I think she said it on some podcast?) that doesn't shy away from acknowledging ways in which stuff is genuinely hard (e.g. https://jvns.ca/blog/2024/03/22/the-current-branch-in-git/ is among the best usability/learnability dissections I've seen).
Yes! Julia is fantastic at explaining concepts, and creating ways to learn about them. She produces a great series of “zines” summarizing a bunch of technical topics, her blog archives are really fascinating, and she’s created really useful tools like Mess With DNS (https://messwithdns.net) which gives you your own DNS subdomain and the means to update records so you can try things out in an easy, harmless way.
Strong recommend! Julia's posts are always really engaging and educational.
She also publishes a number of technical topics as ZINES. I bought her "Oh Shit, Git!" zine and learned a ton of useful info, despite having decades in the industry.Zines are a great way to encourage book-allergic coworkers into learning great material.
