michjedi
Joined 10 years ago
Joined 10 years ago
It's not easy to even ask for a book. I've asked librarians for books before, and I even have a friend currently working in a fairly large library request a book for me (with evidence from me of interest in it), and the request was refused.
The real books worth reading are those that make you a greater expert than the majority of the population. Libraries don't benefit much from storing books that will only be read by one or two people.
I read about 30-100 work-related books a year and have seen salary double every few years.
I try to alternate books between different areas, more or less from these sections:
Technical(on currently used technology)
Technical(unused tech vaguely related and may be useful)
Management
Domain(scientific textbooks, best to search from related university course reading lists)
Biographies/accounts/events of typical and super users
"Scientists discover it is turtles all the way down" Bad: 0.9998 - Good: 0.0002
"Planning sophisticated covfefe" Bad: 0.0002 - Good: 0.9998
1. "Apple introduces 16-inch MacBook Pro, the world’s best pro notebook" Bad: 0.9964 - Good: 0.0038
2. "Developing open-source FPGA tools" Bad: 0.3381 - Good: 0.6652
3. "Show HN: Can a neural network predict if your HN post title will get up votes?" Bad: 0.0598 - Good: 0.9307
4. "How internet ads work" Bad: 1.0000 - Good: 0.0000
5. "More Intel speculative execution vulnerabilities" Bad: 0.7413 - Good: 0.2306
6. "OpenSwiftUI – An Open Source Re-Implementation of SwiftUI" Bad: 0.9994 - Good: 0.0005
7. "How VCs Make Money" Bad: 0.9997 - Good: 0.0003
8. "OpenBSD: Why and How (2016)" Bad: 0.9988 - Good: 0.0013
9. "The Perl Master Plan: How to Put Perl Back on Top" Bad: 0.9997 - Good: 0.0003
10. "Jerry (YC S17) Is Hiring Senior Software Developers (Toronto)" Bad: 0.3142 - Good: 0.6800
So all in all, only 3 of today's top 10 had good titles... Either the titles could have been better but the content was too interesting, or this tool has very low recall.
They may be able to pinpoint a location - the barber where all the DNA matches go to have a haircut, and a time - soon after all those with majority DNA in the pile of hair went to the Barber's. Then they can analyse who was in the area around the time and find a person involved.
"Please don't use Hacker News for political or ideological battle"