I've been using this prompt on articles that generate debate. Like microservices, or jwt's. It brings up some interesting points for this article...
Look at this article and point out any wording that seems meant to push a certain viewpoint. Note anything important the author leaves out, downplays, or overstates, including numbers that seem cherry-picked or lack context. Clearly separate basic facts from opinions or emotional language. Explain how people with different viewpoints might read the article differently. Also call out any common persuasion tactics like loaded wording, selective quotes, or appeals to authority.
Oh this is great news. After a $1000 bill running a model on vertex.ai continuously for a little test i forgot to shut down, this will be my go to now. I've been using Cloud Run for years running production microservices, and little hobby projects and i've found it simple and cost effective.
I'm in the same boat. I think i was geocities.com/Soho/???? right when it came out. I had Red Sox trivia questions, and it was multiple choice. The wrong answers linked to wrong.html, and the correct answer linked to 1.html, then 2.html etc. Fun times being a kid on the information super highway.
i remember you had a script that created animated images before that even was a thing. It exploited some kind of quirk in Netscape, must have been 1994-1996?
The first $5 i ever made online was on Compuserve. I was walking home from school (i think 1994) and i found a used Boston Bruins ticket stub on the ground. I put it on the classifieds section and sold it. The buyer sent me a $5 bill in the mail.
Just relying on blocking specific words isn't the best fix. You've gotta attack this problem from different angles. Big names like Doordash or Uber blast messages all the time, so you need a way to tell them apart from new senders who suddenly flood you. The bad guys will switch senders fast, so now you gotta find a way to fingerprint messages. They'll even tweak the text slightly, so you need fuzzy fingerprinting techniques. And then there's the headache of defining what's spam. Some folks see political donation requests or marketing pitches as spam, while others don't. Sorting these messages becomes a puzzle. Then you have to ask yourself if spam and scam are two distinct types of messages. Spotting scam messages means digging into their content, like checking URLs to sniff out if they're fishy. And phishing? That stuff looks real and can play out across a whole message chain.