They weren't lied to. The US Government AND Springfield, OH explicitly and proudly accepted these Haitians into the US during a very difficult time in their country. The Biden Administration specifically encouraged this. [^0]

There are lots of refugees in the US from everywhere in the world. That the ankle bracelet ATD (alternative to detention) is SPECIFICALLY targeted at the same group of refugees that were accused of eating dogs during the last re-election is what makes this problematic...

[^0] https://www.cnn.com/2024/09/19/us/springfield-ohio-haitians-...

[^1]: https://apnews.com/dfcdc6302e154753a526c04706df45d6

They came legally in an effort to bolster Springfield's economy. From Wikipedia:

> As new businesses opened and a labor shortage emerged,[21][23] Haitian immigrants began arriving in Springfield around 2017 to work in local produce-packaging and machining factories.[24] By 2020, Springfield's economy had somewhat rebounded; new employers had added some 8,000 jobs, adding to the labor shortage.[25][26][27][28] Employers encouraged Haitians to continue coming to the city to fill jobs in warehouses, manufacturing, and the service sector. Haitians in the United States, Haiti, and South America heard from family and friends about Springfield, its low cost of living, and its need for workers. Immigration increased substantially after the start of the COVID-19 pandemic, coinciding with deepening political and economic instability in Haiti after the assassination of the president in 2021.[21][24] By 2024, some 12,000 to 15,000 immigrants, many Haitian, had legally moved to the Springfield area.[29][30][21][31] In June 2024,[32] ongoing crises and severe violence in the Caribbean nation of Haiti led the U.S. Department of Homeland Security to grant temporary protected status to Haitian immigrants,[33] allowing the existing migrant population in the U.S. to live and work *without the risk of deportation*

Haiti has a Level 4 "DO NOT TRAVEL" advisory from the State Dept. It is extremely unsafe to go there and has been for many years.

[^0] https://en.wikipedia.org/wiki/Springfield_pet-eating_hoax

- AND and OR still work, as do parentheses. Very useful when combined with the above.

- before: and after: are super useful for narrowing down a result to within a certain timeframe, like before AI. (Sadly I use this all of the time)

- filetype:$EXTENSION for finding files. I used to use this and "index of" to find interesting PDFs on misconfigured web servers, but the Internet has caught on.

- -minus Operator to exclude results that contain a term. Great for excluding subreddits

- inurl:$SUBSTRING or intitle:$SUBSTRING to scope results to pages with $SUBSTRING in their URLs or page titles. Indispensable for finding people on LinkedIn, like recruiters.

Flash cards? Games with micro worlds? To understand ones own codebase???

We figured this out 10, 20, 30 years ago. Small, atomic commits. Small PRs. Lots of manually written tests. Documentation, ideally with the PR.

There is no "understanding" 60k loc highlander PRs. That's an entire feature.

We had it so good once.

ClusterAPI never got the love that it should. I spent a good amount of time on it at VMware, as Tanzu heavily leverages it for cluster deployment (mostly CAP-A and CAP-V). It's basically kubeadm + the spirit of Terraform, Kubernetes controller edition. There's lots of great enterprise-ready options for centralized k8s cluster fleet management these days, but it works really well for folks that are all-in on GitOps and such.

If you trust them blindly, then the vast experiment improvements are obvious and apparent.

If you don't, or if you're the kind of person that likes to research your sources, LLMs are a speed bump.

> because you can't have the vocabulary to search for it directly unless you already know the answer. Indirect questions such as "framework options to do x and y in z situation in this language". The best you could hope for pre-LLM was to find forum posts asking the same or a vaguely similar question and comparing a lot of options, finding out you picked a dud after spending an hour on it because it's fundamentally incompatible due to reasons, searching again, etc.

I disagree with this. Stack Overflow (pre-moderation insanity) forums and the like was and is great at finding answers to questions like this. Reddit threads were also useful for this sort of discussion. Much learning was had while reading through the comments on my way to the answer. Sometimes, doing that refined or re-aligned what I was looking for, as is common when doing research.

Again, it comes back to faith in LLMs. Sure, I can ask an LLM to give me a comprehensive overview of web serving frameworks for $LANGUAGE. It's up to the user to determine how valid the information being put in front of them is.

LLMs can also be extremely confidently incorrect. Example: I used an LLM recently in "research" mode to outline how a solution I sell stacks up to the next biggest competitor in pricing structures. It gave me a lot of (too much) information in a readily-digestible format, including, surprisingly, the "agreed-upon" price of the competitor's products per SKU.

Pricing for enterprise sales contracts is very dark arts, so I went to the sources attached to the result to confirm those numbers. Lo and behold, the prices I was given were nowhere to be found in any of those articles.

Meanwhile, I used a search engine manually to see if I could find a leaked price book using "filetype:pdf" operators, mostly for grins. Found it in 15 seconds. That still wasn't applicable to what I was looking for, as it was for an industry different from mine, but it was there.

I could have told the LLM to deep search PDFs (despite telling it to "ultrathink"), but at that point, again, what is the point of using LLMs if I can do the work myself?

I actually took a (required) class in middle school that taught us how to use a library. Amongst other things, the librarian taught us how to use Google effectively. Everything I learned then (this was in the early 2000s) still works today, since the process of using a search engine hasn't changed very much since its inception.

So many people never learned (or never cared about learning) how to use a search engine, thus why we're here today.

For example, I was searching for a guide for realigning the v-brakes on my wife's hybrid bike and found this: https://volatacycles.com/how-to-adjust-bike-brakes-rubbing/

Now, this is for disc brakes, so not applicable in my situation. I've never ridden a bike with disc brakes, so I can't vet for the accuracy of this guide. Maybe it's totally right; I'm sure someone reading this will know.

However, just look at this article! Zero pictures for a process that _really_ needs it. It just drones on. Imagine following this guide only to discover that the 12-15 Nm rotor torque the article is asking for is actually too high!

A similar article from before AI would at least have (stolen) pictures describing the process. It would also very likely be shorter. Which creates the rub: if I know how to write something engaging and correcting AI's copy will take _just as much time_ as writing it myself, why would I use AI to do it?

In short, AI content is lazy content. My time is valuable and finite; if I'm reading your stuff, I want to know that you at least tried to give a shit about my time as a reader while producing it.